technology in food industry essay

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The future of the food industry: Food tech explained

Food tech shows how technology can improve the way the world grows, produces, distributes and supplies food by using technology such as ai and automation..

• Amanda Hetler, Senior Editor

As businesses turn to technology to fight inflation and improve efficiency, food tech is reshaping the food industry using the latest technology to manage production, distribution and consumption.

The food industry is worth more than $1 trillion in the U.S. gross domestic product, according to the U.S. Department of Agriculture. With an industry this large, there are several challenges, including food sustainability. Food tech is leading the way in transforming the global food sector.

What is food tech?

Food tech is any technology that improves food production, distribution and supply, and it affects the way people sell, produce and distribute food.

Even though this term may sound new, technology and food have been connected since the Industrial Revolution in the late 1700s and early 1800s . This period led to the emergence of industrialized agriculture and set the standards for farming. During this time, industry leaders and inventors worked together to help increase food production and quality. Major developments included the use of artificial fertilizer, creation of pesticides, development of electric power, and the start of horse-powered and then steam-powered machines.

But in the last few years, food tech has become its own sector with the rise of big data, AI and the internet of things ( IoT ). Food tech helps the food industry to be more sustainable by using IoT in all stages.

Challenges of food sustainability

Food systems are responsible for nearly one-third of the world's greenhouse gas admissions, according to a study by Nature Food. These emissions continue to rise due to land-use change, waste management, raising livestock, production and packaging.

The world also wastes about one-third of its food, according to a report by the Food and Agriculture Organization of the United Nations. Even with all this food waste, the World Health Organization estimates that nearly 828 million people don't have enough food.

Farming, the food industry and people's diets affect the environment with the energy consumed for food production and waste generation. Farming affects the land and strips it of its nutrients. Along with water shortages, the world faces a reduction in arable land to produce food.

Food tech is looking to address some of these issues. Startups are taking the newest technology and applying it to various points of the food cycle to create jobs, reduce hunger, and promote responsible production and consumption.

Impact of food tech

The global food tech market was worth $220.32 billion in 2019, according to Emergen Research , and is estimated to grow to $342.52 billion by 2027.

Food tech is increasing food production to help reduce the rate of hunger and feed the world. Agriculture is becoming more automated by using digital and advanced technology to produce food and raw materials with smart farming . Some uses of technology in food production include the following:

• Genetically modified organisms. GMOs are inserted in a plant's genes to help it become disease resistant and grow in areas not favorable for production. GMOs are used in large crops such as rice, wheat and corn.
• Drones. Drones can provide satellite imagery to monitor crop growth and deal with problem areas.
• Meat industry technology. AI is effective in poultry production where it helps detect health issues with birds by the sounds they make. AI robots can work at poultry farms to collect eggs or assist with butchering.
• Crop monitoring. Along with the use of drones, AI can detect pests and diseases in crops. Digital apps -- such as AgroPestAlert, Farm Scout Pro and IPM Toolkit -- can help detect pest infestation and changing soil conditions to prevent large losses.
• 3D food printer. Food printers can create food -- such as pizza, snacks and candy -- at a faster pace. AI helps design the layers and structure of the food by placing one ingredient at a time. This could eliminate waste, as leftover ingredients can be reused.

Various sectors in food tech

In addition to lab-grown meats and vertical farms, food tech is a broad ecosystem of technologies that can be divided into subcategories for each food cycle.

Startups work to increase the quality of crops with technologies such as sensors, drones and software that replace manual labor. AI and machine learning are used to understand how plants and fungi grow and how they can grow effectively. Other parts of ag tech include fertilizer management, automated machinery , soil sensors and water solutions.

Ag tech can help farmers practice regenerative agriculture, which goes beyond preserving the environment and aims to actively improve it through agricultural practices. With enhanced data and automation provided by ag tech, regenerative agriculture can mitigate climate change, restore biodiversity and improve the work environment for farmers.

Food science

Startups are researching new ways to develop products that are both environmentally friendly and can address health concerns. Plant-based meat substitutes -- such as Beyond -- are an example of a recent product in this category. Scientists work with high-moisture extrusion and shear cell technology to find meal replacements -- such as using vegetable proteins in place of meat -- for individuals with health issues, and to find ways to remove common allergies such as lactose intolerance.

Food service

Food-related businesses -- including restaurants, cafeterias, hotels and cafes -- are looking at automation to help them run more efficiently. Robotics is being researched for use by restaurants of the future to help prepare and serve food, such as at the 2022 Beijing Olympics .

Restaurants are also using IoT technology to manage supply orders and track ingredients from initial order to arrival. Using sensors, restaurant owners can track the temperature of storeroom shelves and delivery trucks. They can watch the entire journey to ensure all safety standards are followed.

Smart appliances help make cooking easier with the use of meat temperature sensors and set-and-forget technology so staff can maximize their time while they wait for items to cook. Kitchen automation systems can also help chefs manage time and orders by tracking what needs to be cooked or how long something has been in a hot bin.

Businesses face the challenge of transporting food due to supply chain disruptions. With the growing demand for direct delivery to consumers, including restaurant and grocery delivery and meal kits, technology is needed to track and ensure food is packaged and delivered safely.

Consumer services

Consumers are looking to technology apps to improve diets, find restaurants, search for recipes, and track allergy or specialized diet information. For example, there are apps to find restaurants that meet certain dietary requirements so consumers can avoid allergens and follow their diets.

There are also startups working to educate people on their food choices and the benefits of proper nutrition to manage chronic health conditions and other personal fitness goals.

Food safety

Technology can help restaurants, grocers and other food suppliers manage the shelf life of food. This includes using technology to trace ingredients and check for recalls.

Some businesses are turning to blockchain technology to manage their supply distribution. For example, a grocery store can track a package of chicken to ensure it came from an antibiotic-free supplier.

Surplus and waste management

Technology is working to help reduce waste and improve sustainability in the food industry. Technology -- such as LeanPath -- combines software, smart scales and cameras to monitor and calculate food waste in kitchens. Staff uses the scale to classify and weigh all food thrown out, and the software identifies patterns to help minimize waste.

Manufacturers are turning to more sustainable packaging made of biopolymers that can also help extend the shelf life of a product. Drones and smart sensors can oversee display shelves and take inventory in real time to track product movement and shelf life.

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How technology is transforming the food industry.

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When it comes to food, tech isn’t always the first thing that comes to mind. However, technology over the years has changed how we produce and find our food through applications, robotics, data and processing techniques.

According to a recent report from ING, technology helps food manufacturers produce more efficiently for a growing world population. There are 7.5 billion people in the world right now and that means a higher demand for food each year. By using tech to improve processing and packaging, it can improve the shelf life and safety of food.

Robotics and Machines

The use of machines in the food industry also ensures quality and affordability. By using machines, it drives down the costs of keeping the food fresh and increases productivity. According to the report, “The rise of robotics in the food industry is a tangible example of food tech. The number of robots in the European food industry is well over 30,000, while the number of robots per 10,000 employees rose from 62 in 2013 to 84 in 2017. Although Germany is the largest market, robot density is relatively highest in Sweden, Denmark, the Netherlands and Italy.”

Robotic machines can help to eliminate safety issues for the more dangerous jobs in the food industry. In 2016, a tech company rolled out a program for butchery. By using robots to cut the more difficult of the meats, they can save many work injuries. This is just one of the many ways technology can improve the industry.

3D Printing

In the past few years, 3D printing has really taken off across many industries and the food industry is one of them. There have been several applications of 3D printing food from NASA printing a pizza to creating soft foods for those who cannot chew hard food to consume. It opens the door for innovation being able to create many things that we were unable to before while also being able to help with food sustainability. There are many ways 3D printing is shaking up the industry.

Precision agriculture is a major player when it comes to how technology can make a difference. It is the use of GPS tracking systems and satellite imagery to monitor crop yields, soil levels, and weather patterns to increase efficiency on the farm. Not only can they see all that is happening across the fields, but they can also use analysis from the findings to test the soil and the health of the crops. A major way they are doing this is through the use of drones. These drones can locate and identify diseased or damaged crops and tend to them immediately. The use of these robots does not eliminate the need for food workers but helps them be more efficient with their work. With strict product requirements at large volumes and demand for lower pricing, the robotic elements help create a faster environment to produce more goods than regular labor.

Packaging and Waste

One of the biggest concerns for consumers right now is having healthy and sustainable goods. Consumers pay attention to labels and harmful ingredients, especially with social media, there is not much that companies can get away with anymore. Many companies use technology to help them “go green.” By using robotics and digitizing, companies in the food industry are able to find alternatives to plastics and other harmful packaging to the environment.

There are many different ways we are using technology in packaging now from edible packaging, micro packaging and even bacteria fighting packaging .

Consumers are also looking for where companies are sourcing their products and how they are handling their waste. Currently, 40% of America’s food is thrown away each year. With the help of technology, there are strides being taken to reduce that number and utilize the extra food.

One app,  Copia  uses its extensive food waste reduction dashboard to connect businesses with surplus food to local shelters, after-school programs, and other nonprofit organizations.  Copia’s analytic software manages and tracks their surplus to save money and reduce their overall food waste.

With all these advancements in technology, there are so many different ways that it can really change how we produce food. Evolving technology could be the key to eliminating world hunger and solving our waste problem- we will just have to wait and see.

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How Technology Impacts the Food Industry

• Ossian Muscad
• April 21, 2022

Last Updated on January 7, 2024 by Ossian Muscad

Technology has been increasingly prominent in the food industry in recent years. Though it might not be the first thing that comes to mind when you think of food, companies are always looking for new ways to improve and optimize their production.

Sustainability, health, and freshness are becoming more important to consumers, so businesses have to adjust to meet these demands. But just how impactful is technology in the food industry, and what changes can we expect to see in the future? Let’s find out!

Technology and the Food Industry

Technology and the food industry have certainly developed a symbiotic relationship. As the industry evolves, technology adapts and provides solutions for new challenges. In return, the food industry pushes technology to innovate and evolve.

Strengthening Production and Supply Chain

For instance, technology has greatly enhanced food production and distribution. Advanced machinery and automation technologies have increased efficiency, reducing the time it takes to produce food and minimizing waste. Moreover, sophisticated supply chain management systems, fueled by the Internet of Things (IoT) and predictive analytics, ensure that food products reach consumers on time, maintaining freshness and reducing spoilage.

Innovations in Food Safety and Quality

Technology also plays a critical role in ensuring food safety and improving food quality. For example, food safety technology such as blockchain is being used to track food from farm to table, enhancing transparency and traceability in the food supply chain. Additionally, innovations like modified atmosphere packaging (MAP) have extended the shelf life of food, preserving its quality and freshness for a longer period of time.

Enhancing Consumer Experience

Lastly, technology has revolutionized the way consumers interact with the food industry. Technology has made it easier for consumers to access, prepare, and enjoy food, from online food ordering and delivery platforms to smart kitchen appliances that help prepare meals. This has not only improved the consumer experience but also created new opportunities for businesses in the food industry.

How Technology Affects the Food Industry

The presence of technology in the food industry has grown exponentially in recent years. And as technology advances, so too does the food industry’s ability to meet the ever-changing demands of consumers.

Some of the most notable impacts of technology on the food industry include:

• Increased efficiency and productivity in food production: technology has allowed for a more streamlined and efficient production process, from planting and harvesting to manufacturing and packaging.
• Change in food consumption habits: technology has also changed people’s food consumption habits. For example, the rise of online grocery shopping has made it easier and more convenient for people to buy food.
• Greater focus on sustainability and health: with technology, businesses can track and monitor their environmental impact and make changes to reduce their carbon footprint. In addition, technology has also allowed for a greater focus on food safety and quality control.
• Improved food safety and quality control: technology has made it easier to track food safety and quality and identify and recall contaminated products.
• More environmentally friendly practices: technology has allowed businesses to adopt more environmentally friendly practices, such as using solar power or investing in energy-efficient equipment.
• Increased transparency and traceability:   technology has made it easier for businesses to track their ingredients and ensure sourcing from sustainable and ethical suppliers.
• Enhanced customer experience:   technology has allowed businesses to create a personalized and interactive experience, from online ordering and delivery to in-store technology such as self-checkout and digital menus.

As we can see, technology has a positive impact on the industry. And with continued technological advancements, we can only expect these positive impacts to continue. 

Technologies that Impact the Food Industry

The only constant in technology is change, and the food industry is no exception. As new technologies emerge, businesses constantly adapt to stay ahead of the curve. The following technological advancements are currently making an impact in the food industry and will continue to shape the industry’s future:

Robots are crucial in automating many processes in the food industry, from harvesting and sorting to packaging and shipping. They enhance efficiency, reduce human error, and can work non-stop, resulting in increased productivity. Utilizing robots also allows for reallocating human labor to more intricate tasks. 

Drones are used in agriculture for precision farming, helping farmers monitor crop health and irrigation systems, and even assisting in planting and harvesting. They reduce the time and effort required for these tasks and provide detailed aerial data for better farm management. When incorporated into the food supply chain, drones can also help with inventory management and delivery of products to remote or hard-to-reach areas.

Eco-friendly Packaging and Waste Reduction

Innovative technologies have led to the development of biodegradable and eco-friendly packaging materials. In addition, technologies like waste-to-energy conversion and smart waste management systems help reduce waste and promote sustainability in the food industry. As a result, businesses can reduce their environmental impact and meet the changing demands of environmentally conscious consumers.

Blockchain Methods

Blockchain technology is enhancing transparency and traceability in the food supply chain. It allows real-time food tracking from farm to table, ensuring food safety and preventing fraud. At the same time, blockchain technologies promote ethical sourcing and sustainability in the industry.

Smart Sensors

Smart sensors are used for quality control in the food industry. They monitor temperature, humidity, and other critical factors in real time during food storage and transportation, ensuring food safety and reducing spoilage.

3D Printing

3D printing is used in the food industry to create customized food items, particularly confectionery and catering. It allows chefs and food manufacturers to shape food in ways that were not previously possible. With 3D printing, there is also less waste and more efficient utilization of ingredients.

AI-based Solutions

Artificial Intelligence (AI) is used in the food industry for predictive analysis, demand forecasting, and personalizing customer experiences. AI algorithms can analyze large amounts of data and provide insights that help businesses make informed decisions. The power of AI also enables businesses to optimize processes and minimize costs, ultimately increasing productivity and efficiency.

Hackathons, or problem-solving events, are utilized in the food industry to spur innovation and solve industry challenges. They bring programmers, designers, and industry specialists together to create innovative solutions in a limited timeframe. This innovative approach creates a space for generating out-of-the-box ideas, promoting industry-wide progress.

Internet of Things (IoT)

IoT devices are being used to monitor and control processes in the food supply chain. They collect real-time data that aids decision-making, improves efficiency, and ensures food quality. The influence and integration of IoT devices in the food industry are expected to grow further in the coming years as technological advancements continue.

Data Utilization

Data analytics is helping food companies understand customer behavior, optimize operations, and predict trends. It provides valuable insights that drive decision-making and strategy development in the food industry. By utilizing data, businesses can improve the customer experience, increase efficiency, and stay competitive. At the same time, data utilization also promotes sustainable practices by identifying areas for improvement and reducing waste.

Online Marketing

Online marketing tools and platforms are used to reach consumers directly, promote products, and build brand loyalty. They allow food businesses to engage with customers, receive feedback, and personalize their marketing efforts. Through social media, email marketing, and other online strategies, companies can build a solid online presence and connect with customers in new ways.

Benefits of Using Technology in the Food Industry

The integration of technology into the food industry is not only transforming operations but also providing a multitude of benefits. It is revolutionizing how businesses operate – from the farm to the table – by making processes more efficient, safer, and customer-centric. Here are some of the prominent benefits of using technology in the food industry:

Accurate Predictions for Supply and Demand

With the help of AI and machine learning algorithms, food businesses can predict supply and demand trends with greater accuracy. This predictive analysis reduces waste, optimizes inventory, and effectively helps businesses cater to consumer needs.

Reduced Risk of Perishable Goods

Smart sensors and IoT devices allow for real-time monitoring of food items during storage and transportation, significantly reducing the risk associated with perishable goods. These technologies help maintain optimal conditions, ensuring the freshness and quality of food items, thereby minimizing spoilage and waste.

Flexibility in Restaurants

Technology provides restaurants with unprecedented flexibility in their operations. For instance, digital menus allow for swift changes in offerings based on availability, seasonality, or customer preferences. Similarly, online ordering systems provide the flexibility to efficiently manage takeout and delivery orders.

Better Customer Experience

Technology enhances the customer experience in many ways. Online platforms facilitate easy ordering and delivery services, while AI-driven data analytics enable personalized marketing and recommendations. Furthermore, self-ordering kiosks and mobile payment options improve the speed and convenience of service.

Safer and More Sanitized Food Production Environment

Technologies like automation and robotics ensure a safer and more sanitized food production environment. Robots, being non-biological, drastically reduce the risk of food contamination. Additionally, automated cleaning systems provide a thoroughly sanitized production area, contributing to overall food safety.

Predictions for the Future

As we navigate the 21st century, technology’s role in the food and beverage industry is expected to become even more prominent. The integration of advanced technologies is set to revolutionize every aspect of the industry, from production and processing to service and consumption. Here are several predictions that showcase the future trajectory of technology in the food and beverage industry:

Digital Dining Experience

The continual rise in food delivery services and the integration of technology into the dining experience is rapidly transforming the food industry. This shift makes delivery and takeaway services a crucial part of food businesses. Technologies such as online ordering systems, mobile apps, and third-party delivery platforms enable customers to enjoy meals in the comfort of their homes. This not only expands the customer reach of restaurants but also enhances the customer experience by providing convenience and choice.

Keeping an Eye on Virtual Food Trends

Technology also facilitates the monitoring of virtual food trends. Social media platforms, influencer marketing, and online food blogs play a significant role in shaping and spreading food trends. By keeping an eye on these virtual trends, businesses can stay ahead of the curve, adapt their offerings to current customer preferences, and effectively engage with their audience. Additionally, AI-driven algorithms can analyze online data to anticipate upcoming trends, giving businesses a competitive edge.

Streamlining New Staff Onboarding Process

Technology is also expected to streamline the staff onboarding process in the food industry. Digital training programs, e-learning modules, and virtual reality techniques can provide comprehensive and efficient training for new staff. These tools allow consistent training that can be easily updated to incorporate new procedures or information. Moreover, digital onboarding reduces the reliance on in-person training, thus saving time and resources.

Rise in Eco-friendly Packaging

The rise in eco-friendly packaging is a significant technology trend in the food and beverage industry. As environmental awareness grows, businesses are exploring innovative, sustainable packaging solutions. Technologies are being developed to produce biodegradable, recyclable, and edible packaging, reducing the industry’s environmental impact. This move towards eco-friendly packaging meets the growing demand for sustainable practices and paves the way for a more sustainable future in the food industry.

How the Food Industry Can Adapt to these Changes

As the impact of technology in the food industry continues to evolve, businesses must adapt to keep pace with these changes. They must embrace new customer expectations, improve efficiency through technology, and transition to eco-friendly practices. These changes boost operational effectiveness, enhance customer satisfaction, and contribute to a sustainable environment.

Adapt to New Customer Expectations

In the digital age, customer expectations are constantly evolving. Customers now demand seamless online ordering, quick delivery services, and personalized experiences. Food businesses must leverage technology to meet these expectations. AI-driven data analytics can help understand customer preferences and digital platforms can facilitate easy ordering and delivery services. Additionally, interactive digital menus and mobile payment options can enhance the dining experience, meeting the growing customer demand for convenience and personalization.

Improve Efficiency with Technology

Technology can significantly enhance operational efficiency in the food industry. Automation in food production and processing can speed up operations and reduce manual labor. Similarly, AI and machine learning can streamline inventory management by accurately predicting supply and demand trends. This not only optimizes resources but also reduces waste. Moreover, digital platforms can efficiently manage online orders, improving service speed and customer satisfaction.

Transition to Eco-friendly Practices

Transitioning to eco-friendly practices is crucial in today’s environment-conscious society. The food industry can leverage technology to make this transition. For instance, businesses can implement energy-efficient machines and appliances to reduce their carbon footprint. Additionally, innovative technologies are being developed to create eco-friendly, biodegradable, and recyclable packaging options. By embracing these practices, food businesses can contribute to environmental sustainability and appeal to the growing customer demand for environmentally friendly businesses.

Frequently Asked Questions (FAQs)

Q1: how does technology contribute to food safety.

Technology plays a vital role in enhancing food safety. Advances such as food tracking and traceability technology help ensure the quality of food by enabling the tracking of food products throughout the entire supply chain. Furthermore, automated cleaning systems and robotics help maintain high standards of hygiene, reducing the risk of food contamination.

Q2: How does technology impact food waste?

Technology can significantly reduce food waste through innovations like smart packaging and intelligent inventory management systems. These can detect when a product is about to spoil, allowing for timely consumption or disposal. AI can also predict demand trends, ensuring optimal stocking and reducing wastage due to overstocking.

Q3: How is the dining experience changing with modern technology?

With the advent of technology, the dining experience is becoming increasingly digitized. Customers now have the convenience of online ordering, mobile payments, and interactive digital menus. In addition, AI can provide personalized recommendations based on a customer’s past preferences, enhancing the overall dining experience.

Q4: What role does technology play in creating eco-friendly packaging in the food industry?

Technology is central to the development of eco-friendly packaging in the food industry. Researchers use technology to create sustainable packaging solutions such as biodegradable or recyclable materials. There are also innovations in edible packaging that reduce waste and environmental impact.

Q5: How can technology help in staff training in the food industry?

Digital training programs, e-learning modules, and virtual reality techniques are transforming staff training in the food industry. These tools provide:

• Comprehensive, easily updated training.
• Reducing reliance on in-person sessions.
• Saving time.
• Improving the consistency of the training.

Q6: How does technology aid in tracking food trends?

Technology, specifically AI-driven algorithms, can analyze data from social media platforms, influencer marketing, and online food blogs to identify and predict food trends. This can give businesses a competitive edge, allowing them to adapt their offerings to current preferences and engage more effectively with their audience.

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DATAMYTE also lets you conduct layered process audits, a high-frequency evaluation of critical process steps, focusing on areas with the highest failure risk or non-compliance. Conducting LPA with DATAMYTE lets you effectively identify and correct potential defects before they become major quality issues.

With DATAMYTE, you have an all-in-one solution for your food business operations that can help you stay competitive, adapt to changing customer expectations, and contribute to a sustainable future. Book a demo now to learn more.

Technology holds transformative potential for the contemporary food industry, positively impacting every facet – from enhancing customer experience to optimizing operational efficiency, ensuring food safety, reducing waste, facilitating eco-friendly practices, staff training, and keeping pace with ever-evolving food trends.

The use of AI, machine learning, and digital platforms proves instrumental in delivering personalized services, streamlining inventory management, reducing carbon footprint, and predicting demand trends. As we continue to navigate this technologically driven era, integrating these innovations into your food business operations is more than a luxury — it’s a necessity.

While change may seem daunting, embracing these technologies can strategically position your business for success, giving you a competitive edge and aiding in creating a sustainable future. Explore the multitude of ways technology can revolutionize your food industry operations and take that crucial step towards optimization and innovation today.

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Application of Artificial Intelligence in Food Industry—a Guideline

Nidhi rajesh mavani.

1 Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia

Jarinah Mohd Ali

Suhaili othman.

2 Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, 43400 Selangor, Malaysia

M. A. Hussain

3 Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia

Haslaniza Hashim

4 Department of Food Sciences, Faculty of Science & Technology, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia

Norliza Abd Rahman

Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers .

Introduction

Artificial intelligence (AI) is defined as a field in computer science that imitates human thinking processes, learning ability, and storage of knowledge [ 1 ,  2 ]. AI can be categorized into two types which are strong AI and weak AI. The weak AI principle is to construct the machine to act as an intelligent unit where it mimics the human judgments, while the strong AI principle states that the machine can actually represent the human mind [ 3 ]. However, strong AI does not exist yet and the study on this AI is still in progress. The gaming industry, weather forecasting, heavy industry, process industry, food industry, medical industry, data mining, stem cells, and knowledge representation are among the areas that have been utilizing AI methods [ 4 – 11 ]. AI has a variety of algorithms to choose from such as reinforcement learning, expert system, fuzzy logic (FL), swarm intelligence, Turing test, cognitive science, artificial neural network (ANN), and logic programming [ 3 ]. The alluring performance of AI has made it the most favorable tool to apply in industries including decision making and process estimation aiming at overall cost reduction, quality enhancement, and profitability improvement [ 7 ,  12 ].

As the population in the world is rising, food demand is predicted to rise from 59 to 98% by 2050 [ 13 ]. Thus, to cater for this food demand, AI has been applied such as in management of the supply chain, food sorting, production development, food quality improvement, and proper industrial hygiene [ 14 – 16 ]. Sharma stated that the food processing and handling industries are expected to grow about CAGR of 5% at least until 2021 [ 15 ]. ANN has been used as a tool in aiding real complex problem solving in the food industry according to Funes and coworkers [ 17 ], while based on Correa et al., the classification and prediction of parameters are simpler when using ANN, which leads to higher usage demand of ANN over the past years [ 18 ]. Besides, FL and ANN have also acted as controllers in ensuring food safety, quality control, yield increment, and production cost reduction [ 19 ,  20 ]. AI technologies have also known to be beneficial in food drying technology and as process control for the drying process [ 21 – 23 ].

Previous studies have shown many usages of AI in food industries focusing on individual target and aims. A study has been conducted on the various ANN applications in food process modeling where it has only highlighted the food process modeling using ANN [ 24 ]. Apart from that, the implementation of AI such as ANN, FL, and expert system in food industries have been reviewed but specifically focusing on the drying of fresh fruits [ 23 ]. A review has been conducted on how food safety has been one of the main concerns in the food industry which leads to the development of smart packaging systems to fulfill the requirements of the food supply chain. Intelligent packaging monitors the condition of foods to give details on the quality of the food during storage and transportation [ 25 ]. Another study reviewed on intelligent packaging as a tool to minimize food waste where about 45 recent advances in the field of optical systems for freshness monitoring have been reported. Meat, fish products, fruits, and vegetables were covered in the study as they are the most representative fields of application [ 25 ]. Few different studies have been conducted on intelligent packaging, and these studies proved that the usage of intelligent packaging systems plays an important role in the food factory in the context of the food chain as they are able to monitor the freshness of food products and crops [ 23 ,  26 – 30 ].

There are also several other studies that have been conducted on the application of AI and sensors in food; however, the coverage is rather limited. Therefore, a comprehensive review that assembles all AI applications in the food industry as well as its combinations with appropriate sensor will be a great advantage, all of which are unavailable as to the knowledge of the author. Such review will assist in gathering the advantages, limitations, and methodologies as a one-stop guideline and reference for food industry players, practitioners, and academicians. To be exact, different types of AI and their recent application in food industries will be highlighted which comprises several AI techniques including expert system, fuzzy logic, ANN, and machine learning. In addition, the integration of AI with electronic nose (E-nose), electronic tongue (E-tongue), near infrared spectroscopy (NIRS), and computer vision system (CVS) is also provided. This paper is organized as follows. The introduction of AI is explained in the first section followed by the application of different types of AI in the food industry. Following that, the fusion of the AI with the external sensors in the food industry is presented. In the latter part, a critical review is conducted where discussion on the main application of the AI algorithms in the food industry is carried out. A flowchart is presented to assist the researchers on establishing the most appropriate AI model based on their specific case study. Then, the trends on the application of AI in the food industry are illustrated after that section. Finally, a brief conclusion is discussed in this paper.

AI in Food Industry

The application of AI in the food industry has been growing for years due to various reasons such as food sorting, classification and prediction of the parameters, quality control, and food safety. Expert system, fuzzy logic, ANN, adaptive neuro-fuzzy inference system (ANFIS), and machine learning are among the popular techniques that have been utilized in the food industries. Prior to AI implementation, studies related to food have been going on over the years to educate the public about food knowledge as well as to improve the final outcomes related to food properties and the production of foods [ 31 – 36 ]. A lot of benefits can be obtained by using the AI method, and its implementation in the food industry has been going on since decades ago and has been increasing till today [ 37 – 39 ,  31 , 32 ]. Nevertheless, this paper will focus on the application of AI in food industries from the year 2015 onwards since tremendous increase and innovation are seen in the implementation recently. It is worth noting that several methods such as partial least square, gastrointestinal unified theoretical framework, in silico models, empirical models, sparse regression, successive projections algorithms, and competitive adaptive reweighted sampling which have been used for prediction and enhancement of the food industries are not discussed here; instead it is narrowed down to the wide application of AI in the food industry.

Knowledge-based Expert System in Food Industry

The knowledge-based system is a computer program that utilizes knowledge from different sources, information, and data to solve complicated problems. It can be classified into three categories which are expert systems, knowledge-based artificial intelligence, and knowledge-based engineering. The breakdown of the knowledge-based system is presented in Fig.  1 . The knowledge-based expert system which is widely used in the industries is a decisive and collective computer system that is able to imitate the decision-making ability of human expert [ 40 ]. It is a type of knowledge-based system that is known as among the first successful AI models. This system depends on experts for solving the complicated issues in a particular domain. It has two sub-systems, which are knowledge base and inference engine. The facts about the world are stored in the knowledge base, and the inference engine represents the rules and conditions regarding the world which are usually expressed in terms of the IF–THEN rules [ 41 ]. Normally, it is able to resolve complicated issues by the aid of a human expert. This system is based on the knowledge from the experts. The main components of the expert system (ES) are human expert, knowledge engineer, knowledge base, inference engine, user interface, and the user. The flow of the expert system is shown in Fig.  2 .

Knowledge-based system

Expert system

The food industry has been utilizing ES for various objectives as this system is proven to be useful especially in the decision-making process. The knowledge-based expert system has been applied in white winemaking during the fermentation process for the supervision, intelligent control, and data recovery [ 42 ]. Apart from that, a web-based application was developed by implementing the ES to calculate the nutritional value of the food for the users, and the development of ES was able to help the SMIs in obtaining the details required for the qualification in obtaining the food production certificates [ 43 ]. Food safety is very important in the food industry,thus, the application of ES that is linked closely to food safety has been used extensively ranging from process design, safety management, quality of food, and risk assessment [ 44 ]. Furthermore, a prototype information technology tool and guidelines with corrective actions that considered ES in the model were developed for the food industry where few essential factors such as food safety, nutrition, quality, and cost were studied [ 45 ]. In addition, a digital learning tool, namely, MESTRAL, was developed to assist people in food processing by using models developed from research in food science and technology and simulators. This tool is based on the knowledge engineering and reflected real applications which can be mapped with the system scale and knowledge frameworks [ 46 ]. A comprehensive review was conducted by Leo Kumar on the application of the knowledge-based expert system in manufacturing planning. The paper has also discussed the utilization of ES in decision making in three wide areas which are the process planning activities, diverse applications, and manufacturing planning [ 41 ]. Moreover, Table ​ Table1 1 gathers some of the recent application of ES in the food industry ranging from the raw material to the final production as well as the food safety.

Application of expert system in food industry

Fuzzy Logic Technique in the Food Industry

Fuzzy logic (FL) was first introduced by Zadeh in 1965 based on the impeccable capability of human intellect in decision making and unraveling the imprecise, uncertain, and ambiguous data while solving problems [ 47 ,  48 ]. The fuzzy set theory is recognized in such a manner that an element belongs to a fuzzy set with a certain degree of membership which has a real number in the interval [0, 1] [ 49 ]. FL models consist of several steps which are fuzzification, inference system, and defuzzification process [ 50 ,  51 ]. Fuzzification is a process where the crisp value is converted into a degree of membership and yields the fuzzy input sets. The corresponding degree in the membership functions is normally between 0 and 1. [ 52 ]. There are a variety of membership functions to choose from, whereby the commonly used ones are triangular, Z-shaped, S-shaped, trapezoidal, and Gaussian-shaped [ 52 ]. The inference system is where the fuzzy input is being translated to get output by using the fuzzy rules. The fuzzy rules are known as IF–THEN rules where it is written such IF premise, THEN consequent whereby the IF comprises input parameters and THEN is the output parameters [ 53 ]. The inference system consists of the style which is either the Mamdani or Takagi–Sugeno Kang (TSK). Defuzzification is the ultimate phase in the fuzzy logic model where the crisp values are obtained [ 54 ]. There are different methods of defuzzification which are center of gravity, mean of maximum, smallest of maximum, largest of maximum, center of maximum, and centroid of area [ 55 ].

FL has been long utilized in the industry due to its simplicity and ability to solve problems in a fast and accurate manner. FL has been employed in the food industry in food modeling, control, and classification and in addressing food-related problems by managing human reasoning in linguistic terms [ 56 ]. The food manufacturing system has improved by the implementation of the fuzzy logic where about 7% of electricity losses has been reduced compared to the conventional regulation method [ 57 ]. Sensory evaluation of the food is also one of the most common parts where FL plays an important role. Furthermore, a quicker solution to problems can be performed by using a system involving fuzzy rules [ 58 ]. Table ​ Table2 2 shows previous applications of FL in the food industry and their attributes. From a previous study, FL has been proven to successfully maintain the quality of the foods, and it acts as a prediction tool and control system for food production processes.

Application of fuzzy logic in the food industry

ANN Technique in the Food Industry

ANN is another AI element, which is also commonly applied in the food industry. ANN is designed to mimic the human brain and be able to gain knowledge through learning and the inter-neuro connections which are known as synaptic weights [ 59 ,  60 ]. Gandhi and coworkers have stated that the configuration of ANN is designed in such a way that it will accommodate certain application such as data classification or pattern recognition [ 61 ]. According to Gonzalez-Fernandez, ANN is applicable to a different kind of problems and situations, adaptable, and flexible. In addition, Gonzalez et al. (2019) have also stated that ANN is suitable to model most non-linear systems and is adaptable to new situations even though adjustments are needed. Moreover, the most outstanding features of ANN is its non-linear regression [ 62 ]. There are several types of ANN including feedforward neural network, radial basis function neural network, Kohonen self-organizing neural network, recurrent neural network, convolutional neural network, and modular neural network [ 63 ]. Multilayer perceptron (MLP), radial basis function networks (RBFNN), and Kohonen self-organizing algorithms are the most effective types of NN when it comes to solving real problems [ 61 ]. The most common network that is used for prediction and pattern recognition is the multilayer perceptron [ 18 , 64 ,  65 ]. Besides that, ANN learning could be classified into supervised and unsupervised depending on the learning techniques [ 17 ]. In general, the structure of ANN consisted of an input layer, hidden layer, and output layer, either single or many layers [ 66 – 68 ]. The architecture comprises activation functions, namely, the feed-forward or feedback [ 69 ]. The backpropagation learning algorithm is normally used as it is able to minimize the prediction error by feeding it back as an input until the minimum acceptable error is obtained [ 18 ]. An additional input known as bias is added to neurons which allows a portrayal of phenomena having thresholds [ 70 , 71 ]. In ANN, the dataset is normally associated with a learning algorithm which trained the network and could be categorized into three groups specifically supervised, unsupervised, and reinforcement learning [ 72 ]. Then, the data will undergo training and testing for analyzing the outputs. The general structure for the ANN is shown in Fig.  3 . The output data can be calculated by using the equation shown based on Fig.  4 .

ANN structure in general

General calculation in ANN

Previous studies have highlighted the utilization of ANN in numerous tasks within the food industry. This includes the assessment and classification of the samples, complex calculation such as heat and mass transfer, and analysis of the existing data for control purposes as well as for prediction purposes which are listed in Table ​ Table3. 3 . All applications have shown satisfactory performances based on the R 2 values, showing that ANN can provide results in an accurate and reliable manner.

Application of ANN in the food industry

Machine Learning Techniques

Machine learning (ML) is known to be the subset of AI [ 73 ,  74 ]. It is a computer algorithm that advances automatically with experiences. ML can be classified into three broad categories which are supervised learning, unsupervised learning, and reinforcement learning [ 11 ,  75 ]. Supervised learning aims to predict the desired target or output by applying the given set of inputs [ 76 ]. On the other hand, unsupervised learning does not have any outputs to be predicted and this method is utilized to classify the given data and determine the naturally occurring patterns [ 77 ]. Reinforcement learning is when there is an interaction between the program and the environment in reaching certain goals [ 78 ]. Among the known models in machine learning are ANN, decision trees (DT), support vector machines (SVM), regression analysis, Bayesian networks, genetic algorithm, kernel machines, and federated learning [ 76 ,  79 ]. ML has been commonly used for handling complex tasks and huge amount of data as well as variety of variables where no pre-formula or existing formula is available for the problem. Other than that, ML models have the additional ability to learn from examples instead of being programmed with rules [ 80 ].

Among the ML methods that are used in the food industry include ordinary least square regression (OLS-R), stepwise linear regression (SL-R), principal component regression (PC-R), partial least square regression (PLS-R), support vector regression (SVM-R), boosted logistic regression (BLR), random forest regression (RF-R), and k-nearest neighbors’ regression (kNN-R) [ 81 ]. Studies showed that the usage of ML has helped in reducing the sensory evaluation cost, in decision making, and in enhancing business strategies so as to cater users’ need [ 82 ]. Long short-term memory (LSTM) which is an artificial recurrent neural network has been employed in the food industry as pH detection in the cheese fermentation process [ 83 ]. On the other hand, GA has been utilized for finding the optimum parameters in food whereas NN has been occupied to predict the final fouling rate in food processing [ 84 ]. ML has shown to be advantageous in predicting the food insecurity in the UK [ 85 ]. Apart from that, ML has also proven to have predicted the trend of sales in the food industry [ 86 ] In addition to that, ML was also able to predict the food waste generated and give an insight to the production system [ 87 ]. Major applications of ML in the food industry and its positive highlights are briefly emphasized in Table ​ Table4 4 .

Application of machine learning in food industries

Adaptive Neuro Fuzzy Inference System (ANFIS) Techniques

ANFIS is a type of AI where FL and ANN are combined in such a way that it integrates the human-like reasoning style of the FL system with the computational and learning capabilities of ANN [ 56 ]. In ANFIS, the learning procedure is transferred from the neural network into the FL system where a set of fuzzy rules with suitable membership functions from the data obtained is developed [ 88 ]. Mamat et al. [ 89 ] stated that uncertainty data could be processed and gain higher accuracy when ANFIS is applied [ 89 ]. Besides, ANFIS is also known as a fast and robust method in solving problems [ 90 ]. Not only that, Sharma et al. [ 91 ] also claimed that ANFIS has a higher performance compared to other models such as ANN and multiple regression models in their study [ 91 ]. ANFIS is a fuzzy reasoning system and combination of the parameters trained by ANN-based algorithms. The fuzzy inference system that is normally used is Takagi Sugeno Kang in the ANFIS model with the feedforward neural network consisting of the learning algorithms [ 92 ]. The structure of ANFIS is made up of five layers which are fuzzy layer, product layer, normalized layer, defuzzification layer, and total output layer [ 93 ,  31 , 32 ]. The backpropagation algorithm has been normally applied in the model in order to avoid over-fitting from occurring [ 92 ]. A high correlation value ( R 2 ) indicates that the developed model has high accuracy and is suitable for industrial applications. The general structure of the ANFIS model is illustrated in Fig.  5 .

General structure of ANFIS

The first layer in ANFIS has nodes that are adjustable, and it is called as the premise parameters [ 56 ]. The second layer in ANFIS has fixed nodes, and the output is the product of all incoming signals. Every output node represents the firing strength of the rule. The third layer consists of fixed node labeled as N. The outputs of the third layer are called normalized firing strengths. Every node in the fourth layer is an adaptive node with a node function, and the parameters in this layer are called as the subsequent parameters [ 56 ]. The final layer in the ANFIS layer has a fixed single node which calculates the overall output as the summation of all the incoming signals. The calculation involved in each layer is shown below. The output of the ith model in layer 1 is denoted as 0 1 , i.

Layer 1: O 1 , i = μ Ai x , f o r i = 1 , 2 atau O 1 , i = μ B i - 2 y , f o r i = 3 , 4 .

Layer 2: O 2 , i = w i = μ Ai x μ Bi y , f o r i = 1 , 2 .

Layer 3: O 3 , i = w ¯ = w i w 1 + w 2 , i = 1 , 2 .

Layer 4: O 4 , i = w ¯ f i = w ¯ i ( p i x + q i y + r i ); w i is the normalized firing strength from layer 3 and.

{ p i , q i , r i } is the parameter set of this node.

Layer 5: O 5 , 1 = ∑ i w ¯ i f i = ∑ i w i f i ∑ i w i .

The ANFIS model is attractive enough that it could solve problems related to the food industry, which are complicated, practical, and barely solved by other methods and has been widely used in the food industry for prediction and classification purposes. ANFIS has been applied in various food processing involving recent technology which comprised five main categories which are food property prediction, drying of food, thermal process modeling, microbial growth, and quality control of food as well as food rheology [ 56 ]. The utilization of ANFIS in the food industry has been commenced years ago, and Table ​ Table5 5 describes those applications.

Application of ANFIS in the food industry

Integrating AI with External Sensors for Real-time Detection in Food Industry

FL or ANN is often integrated with several sensors for real-time detection such as electronic nose (E-nose), electronic tongue (E-tongue), machine learning (ML), computer vision system (CVS), and near infrared spectroscopy (NIRS) for real-time detection and to obtain higher accuracy results in a shorter time. These detectors have also combined their elements together for enhancing their accuracy and targeted results. The integration of these sensors with the artificial intelligence methods has been shown quite a number in food industries over the past few years.

Electronic nose also known as E-nose is an instrument created to sense odors or flavors in analogy to the human nose. It consists of an array of electronic chemical sensors where it is able to recognize both simple and complex odors [ 94 ]. E-nose has been used in gas sensing where the analysis of each component or mixture of gases/vapors is required. Besides, it plays an important role in the food industry for controlling the quality of the products. Due to its ability to detect complex odors, it has been employed as an environment protection tool and detection of explosives materials [ 95 ]. An array of non-specific gas sensors is known to be the main hardware component of E-nose where the sensors will interact with a variety of chemicals with differing strengths. It then stimulates the sensors in the array where characteristic response is extracted known as a fingerprint [ 94 ]. The main software component of E-nose is its feature extraction and pattern recognition algorithms where the response is processed, important details are elicited and then chosen. Thus, the software component of the E-nose is greatly important to stimulate its performance. In general, E-nose is divided into three main parts, namely, sample delivery system, a detection system, and a computing system. ANN, FL, and pattern recognitions are the examples of the methodology employed in E-nose [ 96 ]. The general system of E-nose is shown in Fig.  6 .

E-nose system

E-nose has been widely used to aid in both quality control and assurance in the food industries. Wines, grains, cooking oils, eggs, dairy products, meat and dairy products, meat, fish products, fresh-cut and processed vegetables, tea, coffee, and juices have successfully applied e-nose for sampling classification, detection, and quality control. E-nose has successfully classified samples with different molecular compounds [ 97 ]. Besides, Sanaeifar et al. have reviewed and confirmed that e-nose was able to detect defects and contamination in foodstuffs [ 98 ]. Classification and differentiation of different fruits have also determined by using e-nose [ 99 ]. A review has been conducted on the application of the E-nose for monitoring the authenticity of food [ 100 ]. Adding to this, Mohamed et al. have carried out a comprehensive review on the classification of food freshness by using e-nose integrated with the FL and ANN method [ 101 ]. Recent application of e-nose with computing methods involving AI in food industries is shown in Table ​ Table6 6 .

Application of E-nose with AI in food industries

Electronic tongue (E-tongue) is an instrument that is able to determine and analyze taste. Several low-selective sensors are available in E-tongue which is also known as “a multisensory system,” and advanced mathematical technique is being used to process the signal based on pattern recognition (PARC) and multivariate data analysis [ 102 ]. For example, different types of chemical substances in the liquid phase samples can be segregated using E-tongue. About seven sensors of electronic instruments are equipped in E-tongue, which enabled it to identify the organic and inorganic compounds. A unique fingerprint is formed from the combination of all sensors that has a spectrum of reactions that differ from one another. The statistical software of E-tongue enables the recognition and the perception of the taste. E-tongue comprises three elements specifically the sample-dispensing chamber or automatic sample dispenser, an array of sensors of different selectivity, and image recognition system for data processing (Ekezie, 2015). Samples in liquid forms could be analyzed directly without any preparation while the samples in solid forms have to undergo preliminary dissolution before measurement is carried out. The process of E-tongue system is shown in Fig.  7 below. The ability to sense any taste like a human olfactory system makes it one of the important devices in the food industry, especially for quality control and assurance of food and beverages [ 103 ]. In addition, E-tongue has been used to identify the aging of flavor in beverages [ 104 ], identify the umami taste in the mushrooms [ 105 ], and assess the bitterness of drinks or dissolved compounds [ 102 ]. Jiang et al. performed a summarized review on the application of e-nose in the sensory and safety index detection of foods [ 106 ]. Moreover, the demand of E-tongue in the food industry market has risen due to the awareness on delivering safe and higher-quality products. The details of recent applications of E-tongue in the food industry are shown in Table ​ Table7 7 .

E-tongue system

Application of E-tongue with AI in food industries

The computer vision system (CVS) is a branch of AI that combines the image processing and pattern recognition techniques. It is a non-destructive method that allows the examination and extraction of image’s features to facilitate and design the classification pattern [ 107 ]. It is also recognized as a useful tool in extracting the external feature measurement such as the size, shape, color, and defects. In general, it comprised a digital camera, a lighting system, and a software to process the images and carry out the analysis [ 108 ]. The system can be divided into two types which are 2D and 3D versions. Its usage is not restricted to various applications in food industries such as evaluating the stages of ripeness in apples [ 107 ], predicting the color attributes of the pork loin [ 109 ], detecting the roasting degree of the coffee [ 110 ], evaluating the quality of table grapes [ 111 ], and detecting the defects in the pork [ 112 ]. The combination of CVS with soft computing techniques has been said as a valuable and important tool in the food industry. This is because the combination of these systems offers good advantages such as an accurate prediction in a fast manner can be achieved. Table ​ Table8 8 shows the combination of CVS and soft computing that has been used in the food industry. Figure  8 shows the working principle of CVS. An example on the utilization of CVS for the quality control is shown in Fig.  9 [ 113 ].

Application of CVS with AI in food industries

Working Principle of CVS

CVS-based quality control process

Near infrared spectroscopy (NIRS) is another technique in the food industry as there is no usage of chemicals and results can be obtained accurately as well as precisely within minutes or even continuously [ 114 ]. In addition, it is known to be non-destructive, cost effective, quick, and straightforward which makes it a good alternative for the traditional techniques which are expensive and labor intensive and consumes a lot of time [ 115 ]. The chemical-free method by NIRS makes it suitable to be used as a sustainable alternative since it will not endanger the environment or the human health. It has a wide range of quantitative and qualitative analysis of gases, materials, slurries, powders, and solid materials. Furthermore, samples are not required to be grounded when light passes through it and certain features or characteristics that are unique to the class of the sample are revealed by the spectra of the light. Complex physical and chemical information on the vibrational of molecular bonds such as C–H, N–H, and O–H groups and N–O, C–N, C–O, and C–C groups in organic materials can be provided by the spectra which can be recorded in reflection, interactance, or in transmission modes [ 114 ].

The basic working principle for NIRS is shown in Fig.  10 . Recently, NIRS has become an interest in food industries to inspect food quality, controlling the objective of the study and evaluating the safety of the food [ 114 ,  116 – 119 ]. Several researchers have applied the NIRS in food to obtain its properties for multiple reasons including determining the fatty acid profile of the milk as well as fat groups in goat milk [ 120 ]. Apart from that, it is able to aid in the prediction of salted meat composition at different temperatures [ 121 ] and in the prediction of sodium contents in processed meat products [ 122 ]. The detection and grading of the wooden breast syndrome in chicken fillet in the process line was also able to be performed by using the NIRS technique [ 123 ]. Not only that, it is proven to be efficient in determining the maturity of the avocado based on their oil content [ 124 ], predicting the acrylamide content in French-fried potato and in the potato flour model system [ 125 ], and determining the composition of fatty acid in lamb [ 126 ]. There has been a review conducted on the application of the ANN combined with the near-infrared spectroscopy for the detection and authenticity of the food [ 127 ]. The ability of the NIRS system in detecting the physical and chemical properties coupled with soft computing techniques such as ANN, FL, and ML allows the classification and prediction of the samples to be performed rapidly and accurately. Table ​ Table9 9 shows the application of NIRS coupled with AI techniques in the food industry.

Basic working principle of CVS

Application of NIRS with AI in food industries

Summary on the Application of AI in the Food Industry

From the review so far, it can be shown that AI has been used for various reasons in food industries such as for detection, safety, prediction, control tool, quality analysis, and classification purposes. Ranking of sensory attributes in the foods can be done easily by using the FL model. Not only that, fuzzy logic can be used for classification, control, and non-linear food modeling in the food industry. ES is widely used in the food industry for decision-making process. On the other hand, ANN model is applied widely in the food industry for prediction, classification, and control task as well as for food processing and technology. The supervised ANN method has the ability to learn from examples which allows for the prediction process to be done accurately. Meanwhile, the unsupervised method of ANN is found to be more common for the classification task. Another method that has been utilized for the prediction and classification of the food samples is by using the machine learning (ML) method. ML can be used in solving complicated tasks which involves a huge amount of data and variables but does not have pre-existing equations or formula. This method is known to be useful when the rules are too complex and constantly changing or when the data keep changing and require adaptation. Furthermore, the adaptive neuro fuzzy inference system (ANFIS) is another hybrid AI method that can be used to solve sophisticated and practical problems in the food industry. However, decent data are required for the model to learn in order to perform well. In addition to that, this model is useful for solving analytical mathematical models in the food industry such as studies involving mass and heat transfer coefficients. ANFIS is recommended to be used when complex systems where time-varying processes or complex functional relationships and multivariable are involved. Apart from that, it can be used in descriptive sensory evaluation.

These AI algorithms can be combined with other sensors such as the electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy to glean the data from the samples. Both the E-nose and E-tongue have shown to enhance the quality characteristics in comparison to the traditional detection approach [ 128 ]. E-nose can be used to sense the odors or gases while the E-tongue can be applied for the identification of the organic and inorganic compounds. Studies involving the examination and drawing out the features of the samples like shape, color, defects, and size can be carried out by using the CVS sensors. NIRS can be utilized to determine the properties or contents in the samples. The data obtained from these sensors is then merged with the AI algorithms and utilizing their computing strengths to accomplish the desired studies.

Advantages and Disadvantages of AI

AI has been used widely in the industry as it offers a lot of advantages compared to the traditional method. All the algorithms are known to be accurate and reliable, but careful selection should be made by considering the advantages and limitations of the algorithms. The different algorithms have their own strengths and weakness, hence choosing them for a particular application in the food industry needs to be looked on a case-to-case basis. The guideline to choose the most appropriate method is given in the next section. The benefits and constraints that each of the algorithm exhibits are explained briefly in Table ​ Table10 10 .

Advantages and limitations of AI algorithms

Guidelines on Choosing the Appropriate AI Method

Selecting the appropriate algorithm is important when developing the AI model as it can aid the user to attain an accurate, rapid, and cost-saving results. Therefore, a guideline given in Fig.  3 is an important asset prior to achieving best performances in a case study. The primary step in the selection process is that users should define and finalize the objective of using AI in their research or implementation. Prediction, classification, quality control, detection of adulterants, and estimation are among the common objectives of AI applications in the food industries. Next, decision should be made whether sensors such as E-tongue, E-nose, CVS, and NIRS are required to collect the sampling data or not for collecting the data from the samples. Normally, integration with those sensors is conducted to obtain the parameters and characteristics of the samples to be included in the AI algorithms for sample testing purposes. Upon deciding the necessity of the sensors, users should compare and choose the fitting algorithm with respect to their study. Among the most common AI algorithms that have been employed include the FL, ANN, ANFIS, and ML methods. ANFIS has shown to have a higher accuracy, but the complexity of the model makes it less favorable compared to the other algorithms. It is advisable for the users to determine the complexity of the research in selecting the most appropriate algorithm for their studies. Once the selection of the algorithm has been confirmed, the data available are integrated with the AI algorithms. Finally, the testing and validation based on R 2 and MSE are done to analyze the performance of the established model. The AI model has been created successfully once the validation is accepted; otherwise, users should return to the previous step and reselect the algorithm. Figure ​ Figure11 shows 11  shows the guideline in choosing and development of the AI model in food industry application.

Flowchart for developing AI model

Trends on the Application of AI in the Food Industry in the Future

The overall trend on the application of AI in the food industry is shown in Fig. ​ Fig.12. 12 . From the studies within the past few years, the usage of the AI methods has been observed to increase from 2015 to 2020 and is predicted to rise for the next 10 years based on the current trends. Among the rising factors for the application of AI in the food industry is the introduction of Industrial Revolution 4.0 (IR 4.0). The merging of technologies or intelligent systems into conventional industry is what is known as IR 4.0 and can also be called smart factory [ 129 ,  130 ]. AI which is categorized under the IR 4.0 technologies focuses on the development of intelligent machines that functions like the humans [ 131 ]. IR 4.0 makes a great impact in the product recalls due to the inspections or complains in the food industries. The implementation of the AI integrated in the sensors able to detect the errors during the manufacturing process and rectify the problems efficiently. Apart from that, IR 4.0 also plays a big role in the human behavior as consumers in the twenty-first century often discover information regarding the foods in the internet. The rising concerns on the food quality allow more usage of AI as they are able to enhance the quality of the food and aids during the production process. The highest amount of application of AI in the food industry was seen in the year 2020 as more researchers are carrying out studies using the AI method, and it is believed to continue rising for the upcoming years due to increasing in food demand and the concern on the safety of the foods which are being produced.

Application of AI in the food industry

The comparison between the AI integration with and without sensors for real-time monitoring in the food industry is displayed in Fig.  13 . Integration with external sensors has a higher percentage compared to those without the integration of the sensors in the food industries. The purpose of external sensors was to obtain the data from the samples which are then employed into the AI algorithms to carry out various tasks such as classification, prediction, quality control, and others that have been stated earlier. However, the data collection for the year 2017 showed that the percentage for the AI without the external sensors is greater than that with integration with the sensors. This is due to the high amount of research which was conducted without using the external sensors which are listed in this paper. Based on the evaluation carried out during this study, it was found that a high amount of research was done on the integration of CVS sensors with the AI methods. It is explainable as CVS sensors are able to provide important parameters such as the shape, size, colors, and defects which are essential for the quality control in the food industry. However, the integration of the system is mainly dependent on the objectives of the researcher and the industrial players and the availability of the data.

Comparison between integration of AI for real-time monitoring in the food industry

In short, as the AI world is heading towards 2.0 [ 132 ], it can be predicted that the rise in the usage of AI in the food industry is definite and inevitable because of the advantages that they can offer such as saving in terms of time, money, and energy as well as the accuracy in predicting the main factors which are affecting the food industries. Apart from that, in the recent pandemic situation due to the Covid-19 virus, it is predicted that more companies will opt for the usage of AI in their industries to cut down the costs and boost the performance of their company. There have been reports by some of the SMEs that their earnings have dropped and some SMEs have claimed that they could only survive for about 1 to 3 months. The high demand of food and the tight standard operating procedure in the companies during the pandemic situation will encourage the industry players to find an alternative to their problems and AI will be one of them to ensure a smooth operation.

Conclusion and Future Outlook

In conclusion, AI has been playing a major role in the food industry for various intents such as for modeling, prediction, control tool, food drying, sensory evaluation, quality control, and solving complex problems in the food processing. Apart from that, AI is able to enhance the business strategies due to its ability in conducting the sales prediction and allowing the yield increment. AI is recognized widely due to its simplicity, accuracy, and cost-saving method in the food industry. The applications of AI, its advantages, and limitations as well as the integration of the algorithms with different sensors such as E-nose and E-tongue in the food industry are critically summarized. Moreover, a guideline has been proposed as a step-by-step procedure in developing the appropriate algorithm prior to using the AI model in the food industry–related field, all of which will aid and encourage researchers and industrial players to venture into the current technology that has been proven to provide better outcome.

The authors were supported by the Universiti Kebangsaan Malaysia under grant GUP-2019–012.

Declarations

The authors declare no competing interests.

Publisher's Note

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The application of artificial intelligence and big data in the food industry.

1. Introduction

1.1. the early situation in the food industry, 1.2. the current state of the food industry, 1.3. the importance of food safety, 1.4. digital transformation in the food industry, 2. big data in the food industry, 2.1. applications of big data in the food industry, 2.1.1. application of personalized marketing and recommendation system, 2.1.2. consumer behavior analysis and forecasting, 2.1.3. the utilization of big data analytics in supply chain management, 2.1.4. application of forecasting models and machine learning algorithms in demand forecasting, 2.2. the bottleneck of big data applications for the food industry, 2.3. blockchain technology, 3. artificial intelligence in the food sector, 3.1. knowledge-based expert systems in the food industry, the future and challenges of expert systems, 3.2. fuzzy logic systems, 3.3. adaptive neuro-fuzzy inference system (anfis) technology, 3.4. near-infrared spectroscopy technology combined with artificial intelligence, 3.5. application of computer vision systems in the food industry, 3.6. artificial intelligence combined with smart sensors for real-time inspection in the food industry, 4. future trends and challenges for artificial intelligence applications in the food field, 4.1. future development direction and outlook, 4.1.1. application prospects of emerging technologies, 4.1.2. possible directions for innovation and improvement, 4.1.3. exploration of feasibility and sustainability issues, 4.1.4. future challenges ahead, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Ding, H.; Tian, J.; Yu, W.; Wilson, D.I.; Young, B.R.; Cui, X.; Xin, X.; Wang, Z.; Li, W. The Application of Artificial Intelligence and Big Data in the Food Industry. Foods 2023 , 12 , 4511. https://doi.org/10.3390/foods12244511

Ding H, Tian J, Yu W, Wilson DI, Young BR, Cui X, Xin X, Wang Z, Li W. The Application of Artificial Intelligence and Big Data in the Food Industry. Foods . 2023; 12(24):4511. https://doi.org/10.3390/foods12244511

Ding, Haohan, Jiawei Tian, Wei Yu, David I. Wilson, Brent R. Young, Xiaohui Cui, Xing Xin, Zhenyu Wang, and Wei Li. 2023. "The Application of Artificial Intelligence and Big Data in the Food Industry" Foods 12, no. 24: 4511. https://doi.org/10.3390/foods12244511

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Senior Sector Economist, Commodities, Food & Agri

Food tech: Technology in the food industry

Technology is increasingly contributing to food’s journey from the farm to the fork. In this report, we look at the driving forces behind ‘food tech’ and the most promising food tech applications, robotics, data technology and novel processing techniques

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Executive summary

• More and better food through food tech

Technology helps food manufacturers to produce more efficiently for a growing world population. Improving shelf life and food safety revolves around technology, and greater use of machines and software ensures affordability and consistent quality. The importance of technology for manufacturers continues to grow.

• Demanding customers, consumers and society

Food industry customers, like retailers, set stricter product requirements and require larger volumes at low prices. Higher efficiency and labour productivity are essential to remaining competitive. At the same time, consumers and society are demanding in terms of health and sustainability. Robotisation, digitalisation and novel processing methods enable companies to respond to this demand.

• Food industry embraces robotics

The rise of robotics in the food industry is a tangible example of food tech. The number of robots in the European food industry is well over 30,000, while the number of robots per 10,000 employees rose from 62 in 2013 to 84 in 2017. Although Germany is the largest market, robot density is relatively highest in Sweden, Denmark, the Netherlands and Italy.

• Impact of technology on the labour market

Robotisation and digitalisation increase the complexity of production processes and this impacts the labour market. Employees’ duties change and the required level of education increases. Applying food tech, therefore, requires both investments in capital goods and attention to training current staff and recruiting new staff.

The Role of Science, Technology and Innovation in Transforming Food Systems Globally

• Open Access
• First Online: 02 January 2023

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• Robin Fears 5 &
• Claudia Canales 5  

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Although much progress has been made in past decades, the prospects for food and nutrition security are now deteriorating and the converging crises of climate change and Covid-19 present major risks for nutrition and health, and challenges to the development of sustainable food systems. In 2018, the InterAcademy Partnership published a report on the scientific opportunities and challenges for food and nutrition security and agriculture based on four regional reports by academy networks in Africa, Asia, the Americas and Europe. The present chapter draws on new evidence from the regions reaffirming the continuing rapid pace of science, technology and innovation and the need to act urgently worldwide to capitalise on the new opportunities to transform food systems. We cover issues around sustainable, healthy food systems in terms of the whole food value chain, including consumption and waste, the interconnections between agriculture and natural resources, and the objectives for developing a more balanced food production strategy (for land and sea) to deliver nutritional, social and environmental benefits. Our focus is on science, and we discuss a range of transdisciplinary research opportunities that can underpin the UN FSS Action Tracks, inform the introduction of game-changers, and provide core resources to stimulate innovation, inform practice and guide policy decisions. Academies of science, with their strengths of scientific excellence, inclusiveness, diversity and the capacity to link the national, regional and global levels, are continuing to support the scientific community’s a key role in catalysing action. Our recommendations concentrate on priorities around building the science base – including the recognition of the importance of fundamental research – to generate diverse yet equitable solutions for providing sustainable, healthy diets that are culturally sensitive and attend to the needs of vulnerable populations. We also urge better use of the transdisciplinary science base to advise policymaking, and suggest that this would be greatly advanced by constituting an international advisory Panel for Food and Nutrition Security, with particular emphasis on sustainable food systems.

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Innovation can accelerate the transition towards a sustainable food system

Contested agri-food futures: Introduction to the Special Issue

The Food Industry as a Partner for Public Health?

1 introduction: the transformation of food systems.

The world is not on track to meet the Sustainable Development Goal (SDG) targets linked to hunger and food and nutrition security. According to FAO data (FAO 2020 ), the number of hungry people has increased by 10% in the past 5 years and 3 billion people cannot afford a healthy diet. Some countries in Asia and Africa have made significant progress in increasing food and nutrition security alongside reducing poverty in the past decade, but others have not (EIU 2020 ). The risks continue to be compounded by the impacts of population growth, urbanisation, climate and other environmental changes, market instability and economic inequality. Furthermore, the Covid-19 pandemic has exacerbated problems and imposed disproportionate effects on the economically vulnerable, including marginalised groups in urban areas and smallholder farmers in rural areas (FAO 2020 ; EIU 2020 ). However, while there are unprecedented challenges, there are also unprecedented opportunities to capitalise on science, technology and innovation for the purpose of transforming food systems.

In 2018, the InterAcademy Partnership (IAP), the global network of more than 140 academies of science, engineering and medicine, published a global report on food and nutrition security and agriculture, drawing on information from four regional reports prepared by academy networks in Africa (NASAC), Asia (AASSA), the Americas (IANAS) and Europe (EASAC) and emphasising the value of taking a transdisciplinary approach. In the present chapter, we present an update on some of the issues from that global report linked to the assessments made in the chapters in this volume prepared by the regional academy networks for the UNFSS.

The work of the academies has adopted an integrative food systems approach that considers all points along the value chain, encompassing food processing, transport, retail, consumption, and recycling, as well as agricultural production. Moreover, in the transformation of food systems towards economic, social and environmental sustainability, setting agricultural priorities must take account of climate change and pressures on other critical natural resources, particularly water soil and energy, and the continuing need to avoid further loss in ecosystem biodiversity. Interest worldwide in the sustainability of food systems is accelerating (e.g., Global Panel 2020 ; IFPRI 2020 ; Food Systems Dashboard 2020 ; von Braun et al. 2021 ).

In this chapter, which covers the opportunities and challenges for food systems in tackling malnutrition in all its forms (undernutrition, micronutrient deficiencies, overweight and obesity), we frame the contribution that science can make to the local-global connectivity of food systems: (i) to strengthen and safeguard international public goods, i.e., those goods and services that have to be provided at a scale beyond that of individual countries or that can be better achieved collectively; (ii) to understand and tackle environmental and institutional risks in an increasingly uncertain world; and (iii) to help to address the SDGs by resolving complexities within evidence-based policies and programmes and their potential conflicts.

2 Regional Heterogeneity

Inevitably, in a summary of the global position, it is difficult to capture the diversity within and between regions relating to the challenges for food systems. The regional chapters are indicative of the territorial dimension in analysing obstacles to food and nutrition security, emphasising specific contexts for marginalised peoples and smallholder farmers, e.g., for the Hindu Kush Himalayan region (AASSA 2021 ). In Africa, although remarkable progress has been made over the last two decades in reducing extreme hunger, there are increasing pressures on food systems that require radical action (discussed in detail in NASAC 2021 ). Most African Union member states are not on track to achieve the Comprehensive Africa Agricultural Development Plan goals (African Union 2020 ). In the comprehensive publication on country-level data in the Americas that accompanied the regional report on food and nutrition security and agriculture (IANAS 2017 , regional update IANAS 2021 ), there was detailed discussion of diversities within the region and of variation in the social determinants of food and nutrition security, e.g., related to gender. Other regional assessments find moderate-severe food insecurity (SDG Indicator 2.1.2) across the FAO Europe-Central Asia region, varying from 6.7% in the EU to 19% in the Caucasus. Obesity throughout this region is higher than the world average, Footnote 1 a challenge that has been examined by EASAC ( 2021 ).

3 Agriculture-Environment Nexus

IAP defines the desired outcome for food systems as access for all to a healthy and affordable diet that is environmentally sustainably produced and culturally acceptable. The IAP report from 2018 cautioned that an emphasis on increasing total factor productivity (TFP, the efficiency in the use of labour, land, capital and other inputs) is not warranted if such a focus leads to reductions in environmental protection. Since then, there has been continuing interest in using research to leverage TFP for sustainable and resilient farming (e.g., Coomes et al. 2019 ). In particular, the paradox of productivity has been highlighted (Benton and Bailey 2019 ), whereby agricultural productivity may generate food system inefficiency. That is, productivity, when leading to the increased availability of cheaper calories, may help to promote obesity, although nutritional content matters as much as calories. Current global competition policies incentivise producers who can produce the most food for the least amount of money, typically with accompanying environmental damage, including biodiversity loss (Chatham House 2021 ). The strategic focus of research and development, as well as production systems, should shift from staple crops, with the current emphasis on production of a narrow range of calorie-intensive staples, to a balanced strategy for crops that are of more value in terms of nutritional, social and environmental benefits, including fruit, vegetables, seeds, nuts and legumes (as food and feed, NASAC 2021 ).

Reform of food systems requires decision-makers to recognise the interdependence of supply-side and demand-side (including dietary change and waste reduction) actions. There must be further consideration given to strengthening coherence between global agreements, e.g., on responsible investment, and national action (Chatham House 2021 ). And, the continuing food system sustainability challenge of balancing production objectives for agricultural exports with satisfying domestic food and nutrition requirements is an issue for some countries (e.g., IANAS 2021 ).

Current intensive agricultural production depends heavily on fertilisers, pesticides, energy, land and water, with negative consequences for environmental sustainability. Changing environmental conditions and competition for key resources such as land and water provoke violence and conflict, exacerbating the vicious circle of hunger and poverty (NASAC 2021 ). Discussion in the NASAC ( 2021 ) Policy Brief exemplifies some of the particular issues for managing water demand, including conservation and the recycling of waste water, and notes the opportunities for science, technology and innovation in new irrigation schemes. Research and innovation play a crucial role in the transformation to sustainable food systems that produce more efficiently by environmentally friendly means. The options for the convergence of technological and societal innovation (including outputs from biotechnology, AI, digitalisation, and from social and cognitive sciences), exemplified later in this chapter, help to underpin the objectives for sustainable food systems.

Agro-ecology encompasses various approaches to using nature-based solutions for regenerative agriculture innovation (HLPE 2019 ) and systems research is still needed to help strengthen the evidence base for agro-ecological (nature-based) approaches. For example, agroforestry in sub-Saharan Africa has the potential to help tackle health concerns associated with a lack of food and nutrition security (non-communicable diseases) and with human migration, but requires additional research to characterise any increased risk from infectious disease alongside the beneficial outcomes (Rosenstock et al. 2019 ).

Developing diverse and resilient production systems worldwide is important in preparing for the likelihood of cumulative threats from extreme weather events through spillover across multiple food sectors on land and sea (Cottrell et al. 2019 ). In this context, it is relevant to note the interest in the potential of oceans for sustainable economies in addressing food security, biodiversity and climate change. One of the UK Presidency’s core themes for UN FCCC COP26 is “Nature,” with objectives for sustainable land use, sustainable and resilient agriculture, and increasing ambition and awareness of the ocean’s potential. This potential is also of great importance for the UN FSS Action Track on nature-positive production. By contrast with difficulties in expanding land-based agriculture, the potential for the sustainable production of fish and other seafood is increasingly recognised (Lubchenco et al. 2020 ; Costello et al. 2020 ) and brings new possibilities for local livelihoods. Fish supplies provide 19% of the animal protein in African diets (Chan et al. 2019 ; NASAC 2021 ). However, currently, one-third of the world’s marine fish stocks are overfished (FAO 2020 ). Realising the potential of the oceans requires technological innovation and policy reform for fishery management and governance, to restore wild fish stocks, eliminate illegal and unregulated fishing, and ensure sustainable mariculture so as to minimise environmental impacts. Oceans can contribute to climate change mitigation as well as to improved food systems, but it is important to be aware of inadvertent consequences of policy action, e.g., adoption of industrial-scale aquaculture can be associated with rapid growth in GHGs (in China, Yuan et al. 2019 ). Genetic improvement of fish species may help to reduce the environmental footprint of aquaculture (for example, in Africa, where aquaculture has been expanding at a faster rate than in some other places, NASAC 2021 ). This exemplifies a general point about seeking co-ordinated policy across sectors to avoid unintended effects and negative trade-offs. Another example is provided by poorly-designed land use policies to increase bioenergy production, which drive increases in land rent with negative implications for food and nutrition security (Fujimori et al. 2019 ).

4 Delivering Healthy Diets, Sustainably Produced, Under Climate Change

An accumulating evidence base demonstrates that climate change exacerbates food insecurity in all regions by reducing crop yield and nutritional content and by posing additional food safety risks from toxins and microbial contamination (e.g., IPCC 2019 ; Park et al. 2019 ; Ray et al. 2019 ; Watts et al. 2021 ). The effects are most pronounced in those groups who are already vulnerable, e.g., children, because of reduced nutrient intake (Park et al. 2019 ) or a decline in dietary diversity (Niles et al. 2021 ). A systematic review of the literature identified climate change and violent conflict as the most consistent predictors of child malnutrition (Brown et al. 2020 ). By increasing the volatility of risks in the global food system, climate change may also reduce the incentive to invest (IAP 2018 ), and rising heat- and humidity-induced declines in labour productivity reduce the income of subsistence farmers (Andrews et al. 2018 ).

Although better international integration of food trade can be a key component of climate change adaptation at the global scale, it requires sensitive implementation to benefit all regions (Janssens et al. 2020 ): in hunger-affected export-oriented regions, partial trade integration may exacerbate food and nutrition insecurity by increasing exports at the expense of domestic food availability. When assessing trade implications, it is also important to appreciate that climate change presents a risk to global port operations, with the greatest risk being projected for ports located in the Pacific Islands, the Caribbean Sea, the Indian Ocean, the Arabian Peninsula and the African Mediterranean (Izaguirre et al. 2021 ).

There are twin, overarching challenges for food systems: how can they adapt to climate change and, at the same time, reduce their own contribution to it, including in regard to GHG emissions? These intertwined challenges are discussed in all of the regional assessments. Multiple scientific opportunities have been identified to adapt by developing climate-resilient agriculture, e.g., from the application of biosciences to breed improved crop varieties resistant to biotic and abiotic stresses, as well as for the social sciences to understand and influence the behaviour of farmers, manufacturers and consumers in responding to climate change (see, for example, EASAC 2021 ). Combining evidence-based measures will also be essential to mitigate GHG emissions from the sector (currently contributing approximately 30% of global GHGs, Watts et al. 2021 ), including improving agronomic practices, reducing waste, and shifting to diets with a lower carbon footprint. For example, a background paper prepared in 2020 for the Subsidiary Body for Scientific and Technological Advice (SBSTA) of UN FCCC COP Footnote 2 explored agronomic case studies (in South America, Asia, Africa and Europe) for managing nitrogen pollution (including the powerful GHG nitrous oxide) and improving manure management so as to decrease GHGs and benefit the environment. Capitalising on such research requires better connections between science and the broader community, along with relevant policy processes. There is particular need to dismantle obstacles to the transferability of practices and the scaling up of local research results to guide decision-making at the national and regional levels.

One major mitigation opportunity discussed by IAP ( 2018 ) and in all of the regional assessments relates to the potential to adjust dietary consumption patterns so as to reduce GHGs and, at the same time, gain significant potential health benefits (see Neufeld et al. 2021 for discussion of the definition of a healthy diet). For example, there is evidence that reducing red meat consumption, where it is excessive, can improve population health (Willett et al. 2019 ; systematic review of the literature in Jarmul et al. 2020 ). Red meat supplies only 1% of calories worldwide, while accounting for 25% of all land use emissions (Hong et al. 2021 ), though meat is an important source of protein, minerals and vitamins. The policies for reaching such consumption adjustments require more research to actually identify solutions. The proportion of excess deaths attributable to excess red meat consumption is highest in Europe, the Eastern Mediterranean, the Americas and the Western Pacific (Watts et al. 2021 ). However, some populations consume sustainable diets that are meat-based, e.g., the Inuit Indigenous People in the Canadian Arctic: proposals for dietary change must be carefully designed, evidence-based and culturally sensitive in being adapted to circumstances and protecting nutrient supplies for the most vulnerable groups. It should also be acknowledged that the efficiency of livestock production varies according to farming system, such that conclusions, e.g., about the sustainability of pastoral cattle production, may be different from those for feed-lot cattle production (Adeosogen et al. 2019 ; AASSA 2021 ), and that livestock may be the only agricultural activity possible in dryland regions that do not support the cultivation of crops.

Although Africa accounts for the smallest regional share of total anthropogenic GHG emissions, about half of this is linked to agriculture, and the continent is experiencing the fastest increase of all regions (Tongwane and Moeletsi 2018 ; Latin America and South East Asia are also demonstrating rapid growth, Hong et al. 2021 ). As part of the whole systems approach, formulation of mitigation solutions must decouple increases in livestock productivity (and cereal productivity, Loon et al. 2019 ) from increases in GHGs. Progress is being made (e.g., in China, Cui et al. 2018 ; AASSA 2021 ), and decoupling can be informed by better use of the research evidence available, e.g., for improving herd management and animal health, breeding new varieties (with better feed conversion and energy utilisation efficiencies), improving forage provision (e.g., NASAC 2021 ) and strengthening targeted social protection mechanisms, alongside more generic recommendations for dietary change (EASAC 2021 ).

There are unprecedented scientific opportunities coming within range, but there are also multiple obstacles to mainstreaming climate change solutions into food system development planning. Evaluation of obstacles in India (Singh et al. 2017 ) highlights the limited access to finance, difficulties in accessing research and education, and delays in accessing weather information. Systematic review of the literature on smallholder production systems in South Asia (Aryal et al. 2020 ) notes weaknesses in the institutional infrastructure for implementing and disseminating available solutions: the application of science requires institutional change. At the global scale, there is a need for enhanced access to climate information and services around climate-resilient food security actions (WMO 2019 ), e.g., to aid decisions on the most suitable crops and planting times.

5 Responding to Covid-19

Climate change and Covid-19 are converging crises for health in many respects (Anon 2021 ), including food and nutrition security. Observations early in the pandemic Footnote 3 indicated that the production of staple food crops during critical periods (planting and harvesting) was vulnerable to interruptions in labour supply; food processing, transport and retail were also affected early on, particularly the relatively perishable, nutritionally-important fresh fruit and vegetables (Ali et al. 2020 ). Subsequent comprehensive assessment of consequences for global food security (Swinner and McDermott 2020 ) has evaluated how adverse effects on local practice and routines are transmitted to longer-term impacts on poverty and food systems worldwide in increasingly interconnected trade and markets. In some cases, supply disruption has been aggravated by national decisions to restrict the export of food. Footnote 4 The combined effects of Covid-19 in regard to economic recession and food system disruption are particularly detrimental to the poor (Ali et al. 2020 ; Swinner and McDermott 2020 , which includes case studies in Ethiopia, China, Egypt and Myanmar; NASAC 2021 ). However, in some regions, food systems proved relatively resilient (IANAS 2021 ), and there are also examples of good practice in new safety net programmes, including school feeding programmes that should be more widely shared and implemented. Tackling the consequences for child malnutrition has been identified as a particular priority for action (Fore et al. 2020 ), as has attention to gender bias, whereby women are suffering more adverse effects as a consequence of Covid-19-changed household and community dynamics (Swinner and McDermott 2020 ).

As emphasised by EASAC ( 2021 ), the pandemic has exposed the vulnerability of over-reliance on just-in-time and lean delivery systems, globalised food production and distribution based on complex value chains. Therefore, opportunities for increasing the localisation of production systems should be re-examined. However, there is often a mismatch in the timescale needed to adapt to Covid-19 between the imperative for early action to protect vulnerable groups and the relatively slow policy responses (Savary et al. 2020 ). Capitalising on the scientific opportunities may help to minimise this mismatch, e.g., improving food safety and reducing post-harvest losses (IAP 2018 ), implementing evidence-based social protection measures and using Information and Communication Technologies for e-commerce, food supply resilience, early warning systems, and health delivery. Post-Covid-19 initiatives on novel foods, and urban and peri-urban farming systems, can also strengthen food supply chains and create new livelihoods for expanding urban populations, although it is also important to understand and manage inadvertent consequences for rural employment and the environment (Ali et al. 2020 ).

6 Using Science, Technology and Innovation to Promote and Evaluate Action

Continuing with business as usual will not meet the objectives for transformative change. To reaffirm a core message from IAP ( 2018 ): there is urgent need to use currently available evidence to strengthen policies and programmes, and to invest in initiatives to gain new knowledge. Examples of what is possible are discussed extensively elsewhere (e.g., Fanzo et al. 2020 ; Lillford and Hermansson 2020 ). Footnote 5 It is not the purpose here to provide a detailed assessment of transdisciplinary research priorities, but in Table 1 , we map some onto the UN FSS Action Tracks to emphasise new opportunities that are coming within range and the need for science to achieve its potential. Examples are illustrative, not comprehensive; more detail on these and other research priorities are provided in IAP ( 2018 ), the regional chapters and in Sects. 1 , 2 , 3 , and 4 of this chapter. There are also, of course, many interactions between research streams and objectives that cannot be captured in Table 1 .

Several general recommendations can be made:

There is a need to increase the commitment to invest in fundamental science, and then connect that to applications and align it all with development priorities. There is also an important priority to develop improved methodologies for understanding the levers of change, including the attributes of “game-changers.” That is, how to attribute outcomes and impact to investments chosen and scientific or other actions undertaken.

There are new opportunities to improve collaboration and coordination worldwide, as well as build partnerships among the public and private sectors, NGOs and other stakeholders to co-design and conduct research. Transdisciplinary approaches should be encouraged. There is increasing entrepreneurial activity worldwide, e.g., in the Latin America region, a wide range of start-up company activities includes novel foods, novel production systems, and novel approaches to the optimisation of water and other natural resources (IANAS 2021 ). There are also considerable opportunities in Africa for action on agriculture to stimulate economic growth, reducing poverty while also increasing food and nutrition security (Baumuller et al. 2021 ; NASAC 2021 ).

Training and mentoring the next generation of researchers worldwide is essential: academies of science have a key role in encouraging younger scientists.

Obstacles, especially in low- and middle-income countries, in the use and production of data and in the scaling up of applications must be addressed. For example, although big data/mobile-based communications bring significant benefits (e.g., IANAS 2021 ; NASAC 2021 ) and there have been advances in using mobile technology to deliver climate services for agriculture in Africa (Dayamba et al. 2018 ), more should be done to increase access for small-scale farmers (Mehrabi et al. 2021 ). A digital inclusion agenda is needed for governments and the private sector to increase access to data-driven agriculture.

In addition to generating excellent science, it is vital to reduce the delay in translating research outputs into innovation, public policy and practice (IAP 2018 ). Time lags may arise from negative attitudes associated with perceived risks, from excessive regulatory requirements in some countries or from an absence of regulation in others. This leads to fragmentation in the capture of benefits. For example, there is current heterogeneity in considering whether new plant-breeding techniques – such as those based on genome editing – should be included within older legislation governing genetically modified organisms. Scientific advances are occurring worldwide, e.g., collaborative work in Colombia, Germany, France, the Philippines and the USA to develop rice that is resistant to bacterial blight (Oliva et al. 2019 ; IANAS 2021 ). The controversy created by a situation in which regulatory frameworks are disconnected from robust science is discussed by EASAC ( 2021 ). Figure 1 demonstrates the resulting incoherence that acts to deter science, innovation and competitiveness, creates non-tariff barriers to trade and undermines collective action to enhance food and nutrition security. This may have particular adverse consequences for those already suffering malnutrition; for example, the acceptance of gene-based technologies has been mixed in Africa, even though there may be considerable scientific opportunities for using biotechnology in crop breeding programmes to increase resistance to biotic and abiotic stress and to improve nutrient content and nitrogen use efficiency (NASAC 2021 ).

Variation in the regulation of genome editing for plant breeding

7 Strengthening the Contribution of Research to Policymaking

Alongside action to accelerate investment in agriculture and food systems research (von Braun et al. 2020 ), there must be transdisciplinary integration of priorities at the science-policy interface across all relevant sectors (Fears et al. 2019 ), including agriculture, the environment, health and social care, rural and urban development, and fiscal policy. There must also be linkage of policy at the local, regional and global levels (Fears et al. 2020 ), while taking account of local values and circumstances and recognising the challenges for coordination. One recent example from Asia (Islam and Kieu 2020 ) of developing critical mass in regional policy for climate change and food security discusses criteria for successive steps in policy planning, implementation, cooperation and legal obligation, and observes that the latter two steps often present fundamental barriers to moving from the priorities in a national development agenda to regional coherence. In the African region, the recent Joint Ministerial Declaration and Action Agenda (AU 2020 ) calls upon governments to build greater productive capacity in agriculture and strengthen resilience throughout Africa’s agri-food systems.

Scaling efforts for critical mass requires individual countries to recognise that their policy decisions may have an impact on other countries and regions. For example, some countries export their lack of environmental sustainability by increasing food imports from elsewhere (IAP 2018 ).

Academies and others within the scientific community (STCMG 2020 ) have a key role in overcoming obstacles to effective policy by working together across disciplines to show the value of an inclusive approach, e.g., to the SDGs. Moreover, systematic review of the literature indicates that public support for a policy can be increased by communicating evidence of its effectiveness (Reynolds et al. 2020 ; Fears et al. 2020 ). Therefore, the work of academies in using the evidence base to inform policy development and implementation can help to provide the bridge between policymakers and the public.

What are the implications for the UN FSS? UN FSS discussions have highlighted the place of “game-changers” in driving transformative action, and the scientific community has much to contribute in exploring the potential of game-changers to underpin transformation at the science-policy interface (see AASSA 2021 ). For example, a recent commentary on Action Track 1 Footnote 6 identified some key precepts that can be illustrated by academies’ work at the regional and global levels (Table 2 ).

We suggest that there is an additional game-changer, applicable to all Action Tracks: the development of a new international science advisory Panel on Food and Nutrition Security (IAP 2018 ), with a broad remit for food systems, focused on shaping policy choices and strengthening governance mechanisms. A new Panel, recognising the new opportunities and challenges for food system governance, could help to streamline research efficiency in its linkage to policy action and increase the legitimacy of that science advice by using robust assessment procedures (Global Panel 2020 ). The impetus created by the UN FSS requires the coordination and management of food systems by more sectors of government and stakeholders than had been the case for food security, creating an unprecedented opportunity to develop a framework for greater transparency, accountability and the sharing of knowledge. By consolidating the present myriad, fragmented, array of panels and advisory committees, the proposed international advisory Panel could draw on the large scientific community already working on these topics – including academies – and should be asked to address the most pressing issues for transformative change in the face of the mounting global challenges. Food and nutrition security, particularly for high-risk groups, must be a top priority on every country’s national agenda, yet many countries do not have a national security strategy in place (EIU 2020 ). Furthermore, as already noted, advisory capacities, governance policies, and institutions are sometimes weak at the regional level (AASSA 2021 ; NASAC 2021 ). Thus, in addition to building the critical mass for evaluating complex issues at the global scale, an international advisory Panel could help to drive momentum for a national food system strategy in all countries and engender regional-level initiatives in policy development and implementation.

IAP recommends that the UN FSS now consider options for constituting a new international advisory Panel, so as to make best use of the rapid advances in science, technology and innovation, and to motivate evidence-based policymaking at all levels. IAP and its regional academy networks are eager to be involved.

8 Conclusions

Achieving food and nutrition security worldwide by transforming food systems remains a major challenge, compounded by recent pressures from climate change and the Covid-19 pandemic. Actions to promote food systems are relevant to multiple SDGs. It is essential to identify opportunities for synergies and trade-offs while avoiding inadvertent negative consequences, and to engage everybody, in order to enable change. This requires advances in complex food system modelling.

Food systems are diverse and heterogeneous. Continuing research is needed to inform diverse yet equitable solutions for sustainable, healthy diets that are culturally sensitive, focusing on vulnerable groups. That calls for stronger connections between local and international research entities. The opportunities for complex and innovative remote sensing and web-based data should also be explored for this purpose.

Greater transdisciplinarity is needed in research to progress from the current scientific agenda, which is still too often focused on individual components of food systems or on agriculture separate from its environmental context. Social science research must be better integrated with other disciplines, e.g., to understand and inform consumer, farmer and manufacturer behaviours and to guide policies to deliver objectives for social justice. The development of improved methodologies for understanding the attribution of impact is also a critical research priority.

Science is a public good, yet the conduct and use of basic and other research is often fragmented. There is still much to be done to build critical mass worldwide, to share skills and a research infrastructure, and to collaborate in agreeing upon and addressing research priorities and avoiding unnecessary duplication. There is a continued convening role for academies of science to facilitate the exploration of opportunities and tackle the obstacles to research collaboration between disciplines and between the public and private research communities.

There are also opportunities to improve science-policy interfaces and integrate policy development at the local, regional and global levels. One game-changer would be to constitute an international advisory Panel on Food and Nutrition Security with new emphasis on food systems to make better use of the best science to inform, motivate and implement evidence-based policymaking at all levels.

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Acknowledgements

This IAP Brief was drafted by Robin Fears and Claudia Canales in discussion with Volker ter Meulen. We thank Sheryl Hendriks (NASAC), Elizabeth Hodson (IANAS) and Paul Moughan (AASSA) for their helpful advice.

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Fears, R., Canales, C. (2023). The Role of Science, Technology and Innovation in Transforming Food Systems Globally. In: von Braun, J., Afsana, K., Fresco, L.O., Hassan, M.H.A. (eds) Science and Innovations for Food Systems Transformation. Springer, Cham. https://doi.org/10.1007/978-3-031-15703-5_44

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• Published: 09 October 2018

Review of the sustainability of food systems and transition using the Internet of Food

• Nicholas M. Holden 1 ,
• Eoin P. White 2 ,
• Matthew. C. Lange   ORCID: orcid.org/0000-0002-6148-7962 3 &
• Thomas L. Oldfield 1  

npj Science of Food volume  2 , Article number:  18 ( 2018 ) Cite this article

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Environmental impact

Many current food systems are unsustainable because they cause significant resource depletion and unacceptable environmental impacts. This problem is so severe, it can be argued that the food eaten today is equivalent to a fossil resource. The transition to sustainable food systems will require many changes but of particular importance will be the harnessing of internet technology, in the form of an ‘Internet of Food’, which offers the chance to use global resources more efficiently, to stimulate rural livelihoods, to develop systems for resilience and to facilitate responsible governance by means of computation, communication, education and trade without limits of knowledge and access. A brief analysis of the evidence of resource depletion and environmental impact associated with food production and an outline of the limitations of tools like life cycle assessment, which are used to quantify the impact of food products, indicates that the ability to combine data across the whole system from farm to human will be required in order to design sustainable food systems. Developing an Internet of Food, as a precompetitive platform on which business models can be built, much like the internet as we currently know it, will require agreed vocabularies and ontologies to be able to reason and compute across the vast amounts of data that are becoming available. The ability to compute over large amounts of data will change the way the food system is analysed and understood and will permit a transition to sustainable food systems.

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Introduction.

The food we eat today is unsustainable for two reasons: the food system causes unacceptable environmental impacts and it is depleting non-renewable resources. Our food can be regarded as ‘fossil food’ because its production relies on fossil fuel, non-renewable mineral resources, depletion of groundwater reserves and excessive soil loss. The idea of sustainable food systems is at the heart of global efforts to manage and regulate human food supply. 1 The sustainable development goals focus on a number of critical global issues, but Goal 2 (‘end hunger, achieve food security and improved nutrition and promote sustainable agriculture’), Goal 12 (‘ensure sustainable consumption and production patterns’) and Goal 13 (‘take urgent action to combat climate change and its impacts’) are intimately related to the need to transition global food systems from fossil to sustainable. To understand how to meet the challenge presented by these goals, it is necessary to consider what is meant by ‘sustainable’ in the context of a food system. In 1989, the Food and Agriculture Organisation (FAO) council defined sustainable development as ‘the management and conservation of the natural resource base, and the orientation of technological and institutional change in such a manner as to ensure the attainment and continued satisfaction of human needs for present and future generations. Such sustainable development (in the agriculture, forestry and fisheries sectors) conserves land, water, plant and animal genetic resources, is environmentally non-degrading, technically appropriate, economically viable and socially acceptable’. 2 The important ideas in this definition are working within the planetary boundary (‘the natural resource base’), having a future-proof system (‘continued satisfaction’, ‘present and future generations’), limiting impacts to those manageable by the buffering capacity of the planet (‘environmentally non-degrading’), considering the financial needs of business stakeholders (‘economically viable’) and compatible with local needs and customs (‘socially acceptable’).

Five principles have been identified to support a common vision for sustainable agriculture and food. 3 These are: (1) resource efficiency; (2) action to conserve, protect and enhance natural resources; (3) rural livelihood protection and social well-being; (4) enhanced resilience of people, communities and ecosystems; and (5) responsible governance. The aim of this paper is to outline the case for why food systems are not sustainable and to define the case for using technology, specifically internet technologies (hardware and software combined to make the ‘Internet of Food’) to enable the transition of the food system from fossil to sustainable. Increasing population, increasing consumption, a billion malnourished people and agriculture that is concurrently degrading land, water, biodiversity and climate on a global scale 4 combine to indicate that the fossil food systems we currently rely on are not fit-for-purpose. It is suggested that halting agricultural expansion, closing yield gaps, increasing efficiency, changing diets and reducing waste could lead to a doubling of food production with reduced environmental impacts of agriculture. 4 To achieve these changes, it is going to be necessary to harness internet technology, in the form of an ‘Internet of Food’, which offers the chance to use global resources more efficiently, to stimulate rural livelihoods, to develop systems for resilience and to facilitate responsible governance by means of computation, communication, education and trade without limits of knowledge and access.

The concept of ‘Internet of Food’ first appeared in peer-reviewed literature in 2011 (based on a search of scopus.com using ‘Internet of Food’ as the search term). It was described by the idea of food items having an ‘IP identify’, which raised the question of how this might influence our eating habits. 5 Their focus was very much on how the technology could influence food choices and predicted that by 2020 it would be possible to monitor and control food objects remotely through the Internet. It is this technological control of the food system that has real potential to help societal stakeholders (consumers, retailers, processors, producers, shareholders, landowners, indigenous peoples and so on) to engage in the transition of our food system from being fossil to sustainable. The ubiquitous physical tagging and sensing of mass and energy flow in the food system linked to a formal semantic web will allow computation over the whole system to answer questions such as: What was the resource depletion of this product? What is the social impact of eating this product? What food safety procedures have been employed for this product? What and where has wealth been created by the value chain of this product? When these questions can be answered for specific instances of food product types and predicted for new products, then it will be possible to determine whether a specific food system is sustainable or not. The stakeholders demanding answers to these questions are likely to be governance and policy makers and consumers. When these questions can be answered, it will be possible to plan how to manage the evolution of the fundamental life support system (food) from fossil to sustainable in order to support a growing global population.

Current food systems

To understand the need for a systematic transformation of the food system, it is necessary to detail exactly why it is unsustainable. An overwhelming case can be made for the environmental dimension of the system, but there are also social and economic issues as well. This paper will focus the environmental case (resource depletion and adverse environmental impact that relate to the ‘continued satisfaction’ and ‘environmentally non-degrading’ criteria for sustainable food systems), but similar cases can be made for important social and economic issues as well.

Resource depletion

The resource depletion case can be made with respect to energy, nutrients, water, soil and land. Each will be summarised in turn. To date, the agri-food system has converted non-renewable fossil fuel energy into food by enabling mechanisation, amplified fertiliser production, improved food processing and safe global transportation. 6 According to FAO, 7 the agri-food sector accounts for around 30% of the world’s total energy consumption, with Europe alone accounting for 17% of gross energy consumption in 2013. 8 Agriculture, including crop cultivation and animal rearing, is the most energy-intense phase of the food system, accounting for nearly one third of the total energy consumed in the food production chain. 9 To date, renewable energy has had limited penetration of the agri-food sector with fossil fuels accounting for almost 79% of the energy consumed by the food sector. 8 From an energy perspective, the food system can be regarded as unsustainable (cannot meet the ‘continued satisfaction’ requirement) due to its reliance on fossil energy sources.

By the end of the 20th Century, it was estimated that US-produced ammonia represented 32% of fertiliser nitrogen (N) demand, which was produced by extracting N from the atmosphere as ammonia by a process using hydrogen from natural gas (fossil fuel). 9 The vast majority of N fertilisers consumed today are still created using fossil fuels and cannot be regarded as sustainable until such times as new technological approaches emerge, which are currently in their infancy. 10 , 11 A review of mineral fertiliser reserves concluded that potash reserves (the source of most potassium (K) fertilisers) are of great concern and that it is time to start evaluating other sources of K for agriculture 12 but others concluded that ‘modern agriculture is currently relying on a non-renewable resource and future phosphate rock is likely to yield lower quality P at a higher price’. 13 If significant physical and institutional changes are not made to the way we currently use and source P, agricultural yields will be severely compromised in the future. Estimates for when world peak P will be reached range from 2027 14 to 2033. 13 Variations in estimations of when peak P will occur are due the constant changing of reserve levels. 12 The ‘power imbalance’ where just three countries controlling >85% of the known global phosphorus reserves, 15 a concentration of power far greater than that of crude oil, is also of concern, and it has been concluded that the combined impact increasing demand, dwindling reserves and geopolitical constraints could result in reduced production and supply of chemical P fertilisers and increased global P price. 16 It is clear that over time horizons of around 50 years the agri-food system is going to face a major nutrient crisis unless reliance of fossil mineral resources is significantly reduced and ultimately eliminated. From a nutrient management point-of-view, the food system can be regarded as unsustainable (cannot meet the ‘continued satisfaction’ requirement) due to its reliance on fossil mineral resources.

Modern food production is reliant on irrigation to a great extent, which according to the UN water programme, accounts for 70% of freshwater withdrawals worldwide. 17 Excessive removal of groundwater for irrigation is leading to rapid depletion of aquifers in key food-producing regions, such as North-Western India, the North China Plain, Central USA and California. 18 Aquifers replenish so slowly that they are effectively a non-renewable resource. The depletion of these large freshwater stocks threatens food production and security locally and globally via international food trade. Non-sustainable groundwater abstraction contributed to 20% of global gross irrigation water demand in the year 2000 with this demand having tripled over the period 1960–2000. 19 For many countries, irrigation is sustained by non-renewable groundwater, and it has been highlighted that ‘a vast majority of the world’s population lives in countries sourcing nearly all their staple crop imports from partners who deplete groundwater to produce these crops’. 18 Countries who both produce and import food irrigated from rapidly depleting aquifers are particularly at risk, such as USA, Mexico, Iran and China. It has been estimated that India, soon to be the most populous country in the world, will be unable to meet water requirements within 300 years and emerging pressures may reduce this time horizon considerably. 20 Given the interaction of water supply with energy, this situation may become even worse. For example, in California, 20% of electricity production is used for moving and pumping water for agriculture, 21 and as water becomes more difficult to access, the energy demand will increase. From a water management point-of-view, the food system can be regarded as unsustainable (cannot meet the ‘continued satisfaction’ requirement) due to its reliance on non-renewable water resources.

Over 20 years ago, it was estimated that around one third of the world’s agricultural land had been lost to erosion and the rate of loss was about 10 Mha/year 22 Calculations suggest that soil erosion rates under ploughed cultivation are one to two orders of magnitude greater than soil production rates. 23 This rate of soil loss is not compatible with the ‘continued satisfaction’ requirement for a sustainable food system. It is also linked with other environmental impacts, such as loss of carbon, gaseous emissions, non-point source pollution and sedimentation of waterways, 24 therefore it is not compatible with the ‘environmentally non-degrading’ criteria as well. Given projections for expansion of dryland areas to around 50% of total land surface, with 78% of dryland expansion in areas supporting 50% of population growth in the coming decades, 25 the control of soil erosion and its related impacts is going to be a major requirement for sustainable food systems. From a soil management perspective, the food system can be regarded as unsustainable (cannot meet the ‘continued satisfaction’ requirement).

Having considered the energy, nutrient, water, soil and land requirements for food production, it must be concluded that the food system is unsustainable and needs to change because the natural resource base, future satisfaction and environmentally non-degrading requirements cannot be met. It is reasonable to describe food as ‘fossil food’ because of the reliance of non-renewable (and rapidly depleting) resources to supply much of the world’s population. A complete transformation of the food system is required, one that can perhaps be best driven by harnessing appropriate technology to monitor, control and regulate the different types of food system by unleashing the potential benefits of being able to compute over the vast amounts of data that can be obtained from the activities along the food value chain.

Modern industrial agriculture was made possible through land clearing and habitat disruption. Some recognised consequences of this were fragmentation and loss of biodiversity, significant greenhouse gas (GHG) emissions from land clearing and adverse impact on marine and freshwater ecosystems. 26 An estimate suggests that the global food system, from fertiliser manufacture to food storage and packaging, is responsible for up to one third of all human-caused GHG emissions. 27 Using data from 2005, 2007 and 2008, agricultural production is also estimated to be responsible for a significant share of GHG emissions from the food system, releasing ~12,000 Mt CO 2 e/year representing about 86% of all food-related anthropogenic GHG emissions, followed by fertiliser manufacture at ~575 Mt CO 2 e/year and refrigeration at ~490 Mt CO 2 e/year. 28 The impacts of such emissions are already being felt 29 including negative feedbacks on crop yield and health. Reducing this impact will be critical to transitioning from unsustainable fossil food to a sustainable future-proof food system. 28 , 30

The eutrophication of surface waters has become an endemic global problem. 31 From the 1950s to the 1990s, agriculture was associated with a 6.87-fold increase in nitrogen fertilisation, a 3.48-fold increase in phosphorus fertilisation, a 1.68-fold increase in the amount of irrigated cropland and a 1.1-fold increase in land in cultivation. 26 Agricultural production has been identified as a major underlying and persistent cause of eutrophication in many catchments around the world 32 , 33 Nutrient loadings from agriculture are a major driver of water quality deterioration, but it is unclear what level of on-farm control is necessary to achieve water quality improvements. 31 Smart agriculture and precision farming will drive improvement by increasing resource use efficiency and by harnessing technology to determine current conditions, future weather conditions and the correct intervention. 34 , 35

Similar cases can be made for acidification, 36 biodiversity, 37 ecosystem toxicity 38 and other environmental impacts. 39 Taking just the limited number of examples presented above, it is clear that the ‘environmentally non-degrading’ requirement for a sustainable food system is not being met by current food supply systems and a radical change is needed. From an environmental perspective (resource depletion and adverse impact), it can be concluded that food systems are not sustainable (in general), and if we work from a strong sustainability perspective of working within planetary boundaries, 40 they cannot become sustainable until this adverse situation is rectified.

Life cycle thinking methods and the need for an Internet of Food

Life cycle thinking is increasingly being used to assess food system sustainability. 41 It is an approach used to assess products, processes or services in terms of their place in the world, the full life cycle that is required for them to serve human society and environmental, social and economic consequences of that service. The method has been recognised as the leading approach for including sustainability in decision-making in the United States of America, 42 Europe 43 and elsewhere in the world. The quantitative tool used to implement life cycle thinking is life cycle assessment (LCA), which is formalised by international standard (ISO 14040/14044) 44 and has been widely used to assess food production systems. 45 LCA is one of the most important methodologies used to assess the impact (pollution and resource depletion) of the food system by using mass and energy flow accounting to model the system and agreed scientific models to calculate resource depletion and specific types of environmental impacts.

It has been suggested that LCA can lead to practitioners focus on the ‘eco-efficiency’ of inherently unsustainable products, and this can lead to increased consumption, because of the LCA paradigm of reducing negatives rather than increasing positives 46 The cradle-to-cradle (C2C) concept tends to focus more on linking resource consumption and waste creation with sustainability status rather than minimisation of specific impacts. One conclusion is that the best attributes of both approaches should be harnessed. 46 All such methods (e.g. LCA, C2C) depend on being able to collect sufficient data to characterise a system of interest or the use of publicly funded or commercial databases when site-specific data are not available. It was noted that ‘the practicality of adopting LCA to support decision-making can be limited by the generic nature of the assessment and the resource-intensive nature of data collection and life cycle inventory modelling’, 47 which is the key limitation for developing tools to facilitate the transition from fossil to sustainable food. The need to share data between stakeholders in increasingly important for the creation of useful information about the food system.

A number of issues associated with using LCA to better understand and manage food systems have been noted, 41 including (i) the variability of food production, supply chain and consumption globally; (ii) uncertainty related to the specification of data 48 and the system; 49 (iii) identifying the boundary between technosphere and ecosphere because agriculture relies on exploiting the ecosphere; 50 (iv) correctly identifying the real function 51 of the food system in order to select the most useful functional unit; (v) the multi-functionality of the system; (vi) capturing or modelling inventory data (which requires cooperation between stakeholders for food system applications); (vii) the geo-temporal specificity of background data from LCA databases; (viii) capturing the role of different stakeholders (e.g. consumers, government, industry); (ix) the role of diet choices and (x) handling ‘waste’. These issues are seen in the lack of comparability of LCA studies of the same type of product. 52 Furthermore, the scope of LCA as a global tool to quantify environmental impacts over the whole life cycle creates limitations. 53 LCA by its nature, focusses on the global scale and on steady-state, linear homogenous modelling, making it ‘very difficult to include varying spatial and temporal characteristics and nonlinear characteristics of large numbers of processes that occur all over the world’. 53 There are inherent limitations of inventory because of loss of spatial, temporal, dose–response and threshold information, which reduced the accuracy of impact assessments. 54 The ‘Internet of Food’ would transform our understanding of the food system and how they are modelled using LCA, provided data sharing is possible. Of the issues affecting food LCA, 41 most could be directly addressed by the ability to collect data and compute across the whole food chain: variability, uncertainty, multi-functionality, inventory data, databases, stakeholder influence, diet and waste, and the other two, boundary and function, could probably be better understood based on discernible activity. The examples of data mining of U.S. Environmental Protection Agency (EPA) data sets, 47 potential for avoiding excessive simplification 55 and use of big data in industrial ecology 56 indicate that this is the way forward.

Internet of Food: an enabling technology for the transition from fossil to sustainable

The deployment of sensor networks in the food system have historically been stage-specific and typically designed for monitoring and decision-making at a specific site and time, despite the potential for system integration having been recognised more than a decade ago. 57 Many sensors have been developed that could be used for the food chain, for example, for soil monitoring, for precision agriculture purposes, 58 for post-harvest storage monitoring, 59 for process control, 60 for retail logistics monitoring 61 and in some cases for domestic use. 62 A key requirement to create an ‘Internet of Food’ will be to make the data from these sensors interoperable and to be able to compute across the data set they create. A notable limitation is lack of integration caused by the current mix of open and closed data, communications, hardware standards and a lack of willingness to share data between stakeholders. It has been noted that an ‘…ontology-driven architecture for developing hybrid systems [that] consists of various entities including software components, hardware components (sensors, actuators and controllers), datastores (knowledge base, raw data, metadata), biological elements (plants[or animals]) and environmental context…’ 63 would permit the development of precision agriculture applications, and by logical extension this is required to utilise information across the whole food system (i.e. the Internet of Food). The proposal here is that the ‘hybrid-system’ needs to be extended to cover the whole food system, thus permitting production, process, logistics, retail, purchasing, consumption, nutrition and health outcomes to be integrated through information and computation. Where it is not possible to integrate data of the whole system that delivers a product, it will be very difficult to use Internet of Food for best advantage because its strength is determined by the data available.

A critical requirement will be the development of related ontologies. An ontology is the formal naming of concepts (e.g. types, properties, inter-relationships) within a domain and it is used to describe or infer properties of that domain. In order to be able to draw upon a range of data sources (sensors) and databases (knowledge silos), it is necessary to label data with unique identifiers that permit computers to reason with or compute over those data sets. This is where the real value of Internet of Things, and more specifically Internet of Food lies. To achieve the paradigm shift from fossil food to sustainable food systems, such a shift is needed, facilitated by the ability to reason with such data. As noted, 63 an ontology-driven architecture is needed to enable the ‘Internet of Food’. Ecologists have recognised the importance of big data in ecological research 64 in order to address major scientific and societal issues, and to answer the major question facing food (how to achieve a sustainable food system?), an agreed vocabulary and language structure (ontology) is needed. To take simple examples, the word ‘buttermilk’ originally referred to liquid left after churning butter is now also used to describe a fermented or cultured milk drink, so until the language describing these two concepts is standardised it is not possible to compute from diverse data sets within the domain of dairy processing, never mind across domains, where words such as slurry, matrix and texture all mean very different things depending on context. A noted rapid growth of Internet of Things requires standardisation to lower the entry barriers for the new services, to improve interoperability of systems and to allow better services performance. 65 They noted that this is particularly important for security, communication and identification where interoperability, and particularly semantic interoperability, will be critical. It has been recognised that a proliferation of ad hoc coded data systems will be an impediment to developing data-centric systems that can transform farming, 66 so sharing of data, agreement of standards and stakeholder cooperation will be required to achieve food systems transformation.

Food ontologies can be used with the specific aim of identifying gaps and for purposes beyond the initial, relatively simple applications, such as recognising foods, 67 with a contextual focus on diet, food selection, health and wellbeing being possible, 68 which is a critical component of a sustainable food system, and just as important are the social, economic and environmental impacts and benefits. There are untold opportunities to develop specific services targeted in these areas as well as the potential for integration, with tools such as life cycle sustainability assessment to evaluate the true sustainability of specific food products, meal combinations, whole diets and food systems. These ideas have been evaluated in the context of mining U.S. EPA data for assessing chemical manufacturing, 47 which identified that automating data access was a challenge because the data are incompatible with semantic queries. Data need to be described using ontologies to relate those data that need to be linked and to introduce LCA concepts to the descriptions. A framework for integrating ‘big data’ with LCA has been suggested 69 and it was also noted that development of semantic web standards for ecological data have greatly enhanced interoperability in that domain. 70 The same is required for the food system. It has been suggested that when food (and water) domain descriptors have been developed, this will enable ‘IT support [for] improved production, distribution and sales of foodstuffs [and water]’, 71 but the development of the domain models for the food chain is perhaps not a task for commerce or industry, rather for public, international research.

The opportunities that will be created by the Internet of Food are immense. One important shift will be from a descriptive, inferential approach to analysing food systems to a ‘big data’ approach. 68 ‘Big data’ can be characterised in terms of volume (data sets too large for conventional database management), velocity (acquiring, understanding and interpreting data in real time) and variety (the vast array of sources and types of data beyond the conventional rows and columns of numbers describing transactions). 72 Examples have already emerged where ‘big data’ has been used to provide data useful for LCA including agricultural resource survey 73 and resource use and emissions associated with U.S. electric power generation. 74 It is worth pointing out that much of the data relied on for LCA studies is drawn from commonly used databases (e.g. EcoInvent, ELCD, NREL) and are reliant on ‘small data’ and limited observations, which has resulted in reported error (multiple orders of magnitude), 47 while ‘big data’ offers a means to answering questions about environmental impact or food safety that simply cannot be contemplated in the context of controlled experiments. 71

Authors have considered ‘big data’ and ‘internet of things’ in the context of specific parts of the food chain. For example, ‘big data’ in ‘smart farming’ (i.e. the production stage of the food system) is now being used to make predictive insights about farm operations, to support operational decisions, to redesign business processes and to change business models. 75 To leverage this value at the farm level required extension along the food chain beyond the farm, but two scenarios are emerging: closed proprietary systems and open collaborative systems, 74 such as Food Industry Intelligence Network, 76 Food Innovation Network 77 and European Institute of Innovation & Technology (EIT) Food. 78 Priority should be given to development of data and applications infrastructure and at the same time to organisational issues concerning governance and business models for data sharing. 74 In the context of circular economy (i.e. the end of life, non-consumed part of the food system), it was found that, despite the concept of circular economy being discussed for decades, it has not become an adopted business model. 79 An analysis of literature from 2006 to 2015 found only 70 publications at the intersection of circular economy and ‘big data’/‘internet of things’, but nearly half (34) had been published in 2015. 75 It was suggested that technology encompassed by ‘big data’ and ‘internet of things’ is what is needed to enable such change, 75 which is the same argument being put forward here for the Internet of Food in the context of sustainable food systems. Two implications of relevance for the Internet of Food are: there is a gap between scientific research and corporate initiatives, which needs to be closed, and the search of literature was limited by the keywords available, and more specifically the lack of structured taxonomy to describe the circular economy. It is reasonable to conclude that if these ideas are relevant to one small component of the Internet of Food, then they are probably relevant to the concept as a whole.

These two recent reviews highlight the importance of developing the Internet of Food as a precompetitive platform on which business models can be built, much like the internet as we currently know it, and to achieve this we need to define agreed vocabularies and ontologies to be able to reason and compute across the vast amounts of data that are and will be available in the future. The ability to compute over large amounts of data will change the way the food system is analysed and understood. Biological scientists have noted how important data curation is, because as curated data become available the way science is conducted changes. 80 A key requirement of data curation is the connection of data from different sources in a human- and machine-comprehensible way. Another key change is the processing of multiple sources of complex data (‘big data’) using inference programs. While this might lead to new ways of conducting experimental (hypothesis driven) research, it is also unlocking the door to data-driven research, i.e. extracting new knowledge and understanding from data without experimentation or preconceived ideas, and providing new management approaches based on information and better decision-making capabilities. 71

The Internet of Food offers substantial opportunities for understanding the limits and constraints to sustainable food systems and thus supporting decisions about the transition from fossil to sustainable food. It is essential that all stakeholders engage with the development of Internet of Food to ensure harmonious development of a technology that can be used for both pre-competitive applications and commercial exploitation, if it is to be fully developed over the coming 5–10 years. In addition to the technical issues highlighted here, there are considerations of data ownership, privacy, ethical use of data, market control and other application domains (e.g. food safety, traceability, personal nutrition, security, fraud and policy) that need to be developed with stakeholder contributions alongside the technical advances considered here.

Conclusions

In order to transition to a sustainable food system, we need specific technology infrastructure to allow high-quality data to be collected about the food system that will permit the best possible decision-making. Key requirements are: standard vocabularies and ontologies to allow integration of data sets across the internet; proliferation of low cost sensing to allow orders of magnitude change in the supply of empirical observation data into LCA models; and new analytical methods to collate, curate, analyse and utilise data across the whole food production system. We need an Internet of Food to monitor conditions and analyse data to derive knowledge that can be combined with the means to implement control of the system to enable a step change in how we think about food systems. This technology will give us the chance to transition from fossil food to sustainable food systems.

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Acknowledgements

The authors would like to acknowledge the support and funding from the UCD Institute of Food and Health, UC Davis, Food for Health Institute and the IC3-Foods Conference.

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The ideas and initial draft for this paper were drawn together by N.M.H. and consolidated at the inaugural IC3-Foods Conference in UC Davis, November 2016, following extensive discussion with M.C.L. T.L.O. and E.P.W. contributed to background research and additional draft text. M.C.L. refined the technology discussion.

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Holden, N.M., White, E.P., Lange, M.C. et al. Review of the sustainability of food systems and transition using the Internet of Food. npj Sci Food 2 , 18 (2018). https://doi.org/10.1038/s41538-018-0027-3

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Is Food Technology the Future of the Food Industry?

T he global food and food processing industry is on the threshold of a major alteration. Food technologies like bio-innovation, robotics, gene editing, and AI have redesigned the way in which we produce food and manage demand. As the population has increased at a rapid pace, the quantity of agricultural yield has gained a lot of attention. Therefore, industry experts are promoting food technology as the savior of the food industry’s future .

Why food technology? Are we running out of food?

As far-fetched as it sounds, just as with water and agricultural soil, we might run out of food as well. By 2030, per the UN, the global population is expected to number around 8.5 billion. Consequently, there is a need for focused action to increase food yield.

With a third of arable land being degraded and the food sector alone accounting for 70% of global water usage, the threat of running out of food seems to be quite real. And, this could happen sooner than predicted.

The only way to face the glaring threat of food deficit is by deploying sustainable modes of food production . Through food fabrication, nerd farming, and waste recycling, food technology can help all the major stakeholders in the industry to meet the growing demand for food. As a result, food technology is afforded its fair share of funding and R&D.

3D-printed food : An innovative technology with immense potential

By integrating technology and digital gastronomy techniques, 3D food printers manufacture customized food products. Herein, the potential benefits of 3D food printing — like conversion of algae and beet leaves into proteins — are what make it a promising food technology.

3D-printed food also helps save time and effort and is the key to sustainable food creation . Unlike traditional food manufacturing systems, food printers minimize waste with the use of hydrocolloid cartridges that form gels when combined with water.

Already, restaurants like Food Ink and Melissa are innovating their cuisine with the help of 3D food printers. Even hospitals have turned to 3D-printed food to cater to the special needs of their patients. For instance, the Isala Hospital in the Netherlands has switched to 3D-printed meals for their aged care patients. This 3D-printed food has better nutritional content and is customized for patients who have difficulty chewing food.

The takeaway: Food technology is the future, provided… 

Food technology has surely changed the way in which food is produced and consumed. Even though the future does not totally belong to technology-driven food production, these techniques do have a critical role to play. Consequently, in the long term, it will be a mix of government policies and business ethics which will have a major influence on the food industry.

According to our expert analysts, the governments of the world should:

• Provide smaller farmers with capital to invest in technology
• Create an environment which fosters collaboration between farmers, universities, and tech companies
• Encourage tech innovation which will help small farmers in developing economies to bridge the ‘last mile’ problem
• Embrace technologies like big data and satellites to increase the adoption of smart farming techniques .

Source the latest insights into the global food & beverages industry. Download relevant reports  HERE .  

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Literature Review

Summary and critique.

Nowadays, supply chains attract more attention of researchers all over the world as they remain an important part of the business in many fields. Speaking about the industries whose primary aim lies in food supply and distribution, it is important to mention that their role in the life of the human society cannot be overstated. As for the fourteenth chapter of the textbook which has been chosen for the previous assignment, it contains a lot of important information on technical aspects of supply chain management and various technologies facilitating the work of specialists related to the sphere of activity.

Considering that the topic of technology in different industries is extremely significant for the future specialist, it is necessary to pay more attention to the most recent studies devoted to the role of technology and communications in the global food industry. As is clear from the review, traceability systems, RFID, intelligent packaging technologies, and MOLP approach are seen as major enablers in the food supply industry.

In reference to the most recent studies that can shed light on the role of technology and communications as enablers, it is important to mention the work by Aung and Chang (2014) which is devoted to the principles of work of food traceability systems, acting as enablers in the food industry. According to the authors, it is a common truth that various specialists related to the food industry are likely to implement a more customer-oriented approach in order to improve the quality of service and customer satisfaction.

At the same time, considering that modern business is a changing world which tends to swallow up the companies whose employees make crucial mistakes, GTS systems described in the article are extremely important as they enable specialists to spend less time in order to identify possible sources of complaints and prevent distribution and production of goods that do not correspond to basic quality standards. According to the discussed research, traceability is regarded as a tool used in numerous industries that allows checking the quality of a product on a regular basis. Apart from that, the properties of traceability systems tend to vary depending on the type of production.

Thus, working with meat, specialists use the combination of RFID and material handling systems to ensure high quality of distributed production. Just like the authors of the previous article, Piramuthu, Farahani, and Grunow (2013) agree that improvements related to food traceability are supposed to be more effective than labelling. Thus, the former helps to ensure that all goods are authentic and that they do not pose a threat to customers’ health whereas the latter has weaknesses related to this problem.

At the same time, the researchers insist that RFID technologies need to be improved due to the fact that larger production volumes always entail the increasing number of cases of food loss; apart from that, in certain food supply networks, the opportunities for the employees to perform instant information exchange are limited due to different reasons. Therefore, with a lack of improved RFID technology, it becomes more difficult for employees to identify contamination sources as soon as possible and prevent further deterioration of alimentary products. Similarly, the effectiveness of food traceability systems within the context of the global food industry is also confirmed by Kim, Zhang, and Yoon (2016) who claim that the experience of East Asian countries such as South Korea and China indicates that the use of FTS helps to mitigate risks related to product quality.

The general effects of the implementation of RFID that confirm its effectiveness are described by Ha, Song, Chung, Lee, and Park (2014) who have conducted their research on the recent changes within the logistics industry. According to them, the use of RFID helps to create a more collaborative environment for employees and, therefore, acts as an enabler in the food supply industry. Apart from that, as it follows from the conclusions made by Mohammed and Wang (2017), RFID definitely helps to improve performance of companies in food supply industry even though many researchers still find the effectiveness of the technology to be a controversial topic.

Technology helping to improve traceability is also studied in the article by Costa et al. (2013) that provides a substantial review of methods enhancing food control. According to the researchers, traceability is mandatory for the food industry as it acts as a factor ensuring client safety. In particular, the authors focus on the role of RFID in the food industry, claiming that the latter is able to bring numerous benefits to business owners and customers.

As is clear from the analysis conducted by the researchers, RFID technology is becoming increasingly popular when it comes to the food supply, but the opportunities for its implementation are still limited due to economic and technical factors which act as the barriers to progress in different countries. Despite the fact that the authors agree that RFID technologies enable companies to improve the quality of products, they acknowledge that their implementation can entail negative consequences. RFID tools may be too expensive for smaller businesses and their implementation involves the use of additional equipment which may be costly or difficult to find.

According to Bibi, Guillaume, Gontard, and Sorli (2017), RFID technology can be regarded as a tool which is more preferred than the barcode because it can be used to fulfil a wide range of tasks, including the control of external factors (such as humidity or temperature) which may have an impact on product quality. At the same time, the researchers suppose that one of the important objectives for modern developers is the necessity to provide the opportunity for better monitoring with the help of sensors capable of working in combination with RFID tags. To the authors, it seems that the development of improved RFID technology involving the use of sensors acts as a key to the problem of food waste.

Traceability issues related to the food industry are also studied in the work by Dabbene, Gay, and Tortia (2014) which is focused on technologies promoting safety and quality in the food industry. As is clear from the research conducted by the authors, traceability systems present a critical tool for specialists in the field as they significantly increase the effectiveness of food supply chains. In their article, the authors review the approaches to optimise traceability systems to be used in the food industry so that they would act as real enablers. The approaches can be divided into different groups based on the type of goods involved.

For instance, when it comes to perishable foods, the most of the researchers whose works are reviewed suppose that the use of mixed-integer linear programming would be the most appropriate as it helps to optimise traceability systems based on the information concerning expenses. At the same time, the authors state that reducing the costs of product recall remains an important problem in the field. Unlike the authors of the previous article, Soysal, Bloemhof-Ruwaard, and Van der Vorst (2014) focus on the development and implementation of MOLP model which is expected to minimise the costs of logistic operations, make transportation safer for the environment, and, therefore, improve the reputation of a company using this model.

The effectiveness of such an approach is also confirmed by Kannan, Khodaverdi, Olfat, Jafarian, and Diabat (2013) who report the positive results of its implementation in the automobile industry. Despite the different requirements to product quality that are used in various industries, MOLP model has already demonstrated its effectiveness in the case with perishable alimentary products such as meat.

Continuing on the future of technology in food supply, Kumar, Heustis, and Graham (2015) highlight that a little part of technical solutions is thoroughly studied in the United States due to the lack of resources. Within the frame of their research, the authors were focusing on global standards related to a product recall. As is clear from their position, in order to improve product quality and traceability, specialists related to the field should pay more attention to instant communication between employees on different levels.

The packaging is another important problem in the food supply industry. In their work, Vanderroost, Ragaert, Devlieghere, and De Meulenaer (2014) study basic functions of packaging in the food industry and the ways to improve it to ensure better protection. According to them, intelligent packaging is becoming increasingly popular as it can be seen as an enabler in the industry. The use of intelligent packaging can be regarded as a factor enabling business owners to improve HACCP systems used in their companies.

Among other things, the latter is supposed to act as important tools which are used to detect products of inappropriate quality which may be dangerous for customers, reduce the number of health risks for employees and consumers, and single out working processes which may influence the products quality. Understanding that it would be extremely important to create an integrated system that would control FSCs, product flows, and data stream simultaneously, the researchers acknowledge that modern developers are still unable to perform this task. Understanding the great role of sensors in the industry, the authors suppose that their implementation on the global scale is still impossible due to low flexibility of production, high production costs, and relatively low resistance to external factors such as temperature changes in the industry.

Therefore, the question remains open. The interest in intelligent packaging in the field can be illustrated by the fact that numerous researchers have studied the topic. Thus, Dobrucka and Cierpiszewski (2014) have conducted their research to study technologies of food packaging. According to the researchers, the use of technology in the food supply industry is closely interconnected with client preferences that tend to change with unfailing regularity.

As is clear from the analysis conducted by the authors, the most important trends related to packaging technology in the food industry are connected with the development of eco-friendly packaging which is expected to have a reduced impact on the environment and help to improve social image of food suppliers and manufacturers. In their work, the authors describe a number of technical solutions aimed at improving the performance of companies in the industry.

Among them, it is possible to single out the so-called “electronic nose” which propels smell recognition to the next level and helps companies to reduce the number of items of inappropriate quality. As is clear from the article by Fang, Zhao, Warner, and Johnson (2017), the use of intelligent packaging in meat industry helps to improve control of FSCs, minimise quality deterioration of meat products, and prevent the distribution of counterfeited foods. The use of new packaging technologies acts as a factor which improves both the quality of goods and the reputation of a company as proper tools significantly reduce the number of dissatisfied clients and distributors.

At the same time, the development of effective packaging technology is complicated by legislative problems related to safe materials and unstable customer preferences. As is stated by Ghaani, Cozzolino, Castelli, and Farris (2016) in their article, intelligent packaging technologies that possess the largest potential to change the global industry for the better include the use of sensory elements, electronic data carriers such as barcodes, and other devices aimed at improving product safety and customer satisfaction.

Such as authors of many articles that have been mentioned, these researchers believe that the use of intelligent packaging technology provides managers and customers with numerous benefits but there is an important weakness that needs to be paid more attention to during further research in the field: earning capacity for its users should be improved and implementation of these systems should not have an obvious impact on prices of the products as it can entail massive customer outflow.

Evaluating these findings reported by the researchers, it is necessary to notice that there is no significant difference in views when it comes to the effectiveness of the technologies mentioned. In fact, all authors whose works were reviewed tend to highlight the role of RFID, intelligent packaging, traceability systems, and MOLP in global food supply industry as numerous experiments show that all these technologies act as enablers, improve customer satisfaction, and reduce food waste.

Personally, I agree with the points covered by the authors as technologies studied by them are actively used despite the opinions of hard observers, who tend to exaggerate the risks of their implementation. At the same time, the opinions related to questions that need further research tend to be different. For instance, the majority of authors state that cost-effectiveness of RFID systems needs to be improved and additional sensors are to be implemented to reduce food waste on the global scale, and I agree with these points as prevention of food loss is the primary task related to the industry. Speaking of intelligent packaging, different authors single out such problems as the necessity to comply with environmental standards which may be different and the need to reduce costs of implementation of the technology.

To conclude, the fourteenth chapter of the textbook is devoted to the role of technology in supply chains, and the review demonstrates that the primary enablers in global food supply industry include intelligent packaging, the use of RFID technologies, food traceability systems, and MOLP model. In fact, there is a wide range of problems existing in the field that are already solved, but, based on the weaknesses of technologies reported by the authors, it is possible to single out suggestions for further research.

In general, the latter include studying measures, allowing to reduce the costs of these technologies’ implementation so that they could be effectively used all over the world. More than that, future researchers in the field need to pay focused attention to possible improvements to RFID technologies, helping to synchronise them with sensors to prevent possible mistakes.

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Vanderroost, M., Ragaert, P., Devlieghere, F., & De Meulenaer, B. (2014). Intelligent food packaging: The next generation. Trends in Food Science & Technology , 39 (1), 47-62.

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IvyPanda. (2020, November 13). Technology and Communications in the Global Food Industry. https://ivypanda.com/essays/technology-and-communications-in-the-global-food-industry/

"Technology and Communications in the Global Food Industry." IvyPanda , 13 Nov. 2020, ivypanda.com/essays/technology-and-communications-in-the-global-food-industry/.

IvyPanda . (2020) 'Technology and Communications in the Global Food Industry'. 13 November.

IvyPanda . 2020. "Technology and Communications in the Global Food Industry." November 13, 2020. https://ivypanda.com/essays/technology-and-communications-in-the-global-food-industry/.

1. IvyPanda . "Technology and Communications in the Global Food Industry." November 13, 2020. https://ivypanda.com/essays/technology-and-communications-in-the-global-food-industry/.

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Discover 10 Emerging Food Industry Trends in 2025

How are the latest innovations in food technology tackling food shortage and ensuring climate-conscious eating habits? Explore our data-driven industry research on the current food trends based on our analysis of 5000+ companies. These trends include alternative proteins, 3D food printing, personalized nutrition, food robotics & more.

The food industry trends indicate a shift towards sustainable and personalized food choices, including alternative protein sources, local foods, nutraceuticals, and personalized nutrition. Concerns over environmental impacts drive FoodTech startups and brands to integrate waste reduction practices and zero-waste workflows. The COVID-19 pandemic also accelerated the adoption of food industry 4.0 technologies to improve food quality and efficiency throughout the food value chain. Further, food producers digitize production floors with robotics, eCommerce, and digital food management while restaurants employ robots for hospitality and cooking. These advances address the continuing impact of the COVID-19 pandemic as well as foster efficient, transparent, and sustainable operations within the food industry.

This article was last updated in August 2024.

What are the latest trends in the food industry?

• Alternative Proteins
• Nutraceuticals
• Food Safety & Transparency
• Personalized Nutrition
• Restaurant Digitization
• Digital Food Management
• Food Waste Reduction
• 3D Food Printers

Innovation Map outlines the Top 10 Food Industry Trends & 20 Promising Startups

For this in-depth research on the Top Food Technology Trends & Startups, we analyzed a sample of 5065 global startups & scaleups. This data-driven research provides innovation intelligence that helps you improve strategic decision-making by giving you an overview of emerging technologies in the food industry. In the Food Tech Innovation Map, you get a comprehensive overview of the innovation trends & startups that impact your company.

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These insights are derived by working with our Big Data & Artificial Intelligence-powered StartUs Insights Discovery Platform , covering 4.7M+ startups & scaleups globally. As the world’s largest resource for data on emerging companies, the SaaS platform enables you to identify relevant technologies and industry trends quickly & exhaustively.

Tree Map reveals the Impact of the Top 10 Food Technology Trends in 2025

The Tree Map below illustrates the Top Food Trends that will impact companies in 2025. Consumers shifting to alternative protein sources, including lab-grown food, is the most significant trend in the food industry. Further, the increased awareness of nutrition and health concerns during the pandemic drives the demand for nutraceuticals and personalized nutrition. Food e-commerce is another big trend that is fueled by the COVID-19 situation. Also, there is a substantial rise in food safety concerns directly impacting food transparency across the food value chain.

With digitization being an integral part of the food and beverage industry, companies are adopting restaurant digitization, digital food management, and food robotics to improve operations. Food brands are also focussing on food waste reduction and are adopting zero-waste practices. 3D food printers are also a part of the trends in food science and technology, enabling many other major trends such as meat alternatives and personalized nutrition.

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Global Startup Heat Map covers 5065 Food Technology Startups & Scaleups

The Global Startup Heat Map below highlights the global distribution of the 5065 exemplary startups & scaleups that we analyzed for this research. Created through the StartUs Insights Discovery Platform , the Heat Map reveals that the US is home to most of these companies while we also observe increased activity in Europe, particularly in the UK and France.

Below, you get to meet 20 out of these 5065 promising startups & scaleups as well as the solutions they develop. These 20 startups were hand-picked based on criteria such as founding year, location, funding raised, and more. Depending on your specific needs, your top picks might look entirely different.

Top 10 Trends in the Food Industry (2025)

1. alternative proteins.

Consumers are increasingly shifting towards alternative protein sources , driven by health and environmental considerations. This makes it a prominent trend in food technology. These alternatives include cultured meat, lab-grown food, plant-based nutrition, edible insects, and mycoprotein. They have high nutritional richness and resource efficiency from production to consumption, a marked contrast to traditional livestock-based protein.

Further, alternative protein sources offer cost advantages due to their minimal dietary requirements and health monitoring. The ongoing advances in 3D printing, fermentation, and molecular biology also empower startups to develop sustainable solutions for protein production. This, in turn, allows food companies to address ethical concerns and reduce the carbon footprint associated with conventional meat production.

The Protein Brewery offers Animal-Free Protein

The Protein Brewery, a Dutch startup, develops FERMOTEIN , a proprietary animal-free lab-grown food. The startup brews FERMOTEIN using non-allergenic crops and fungi, infusing them with essential amino acids and fiber. This protein alternative has a 10% fat content and high water-binding capabilities, resulting in a meat-like flavor profile.

FERMOTEIN stands out as a natural ingredient, comprising solely brewed fibrous protein. The startup’s solution enables sustainable food developers to mitigate additional protein processing and expedite food product development.

ento manufactures Insect-based Protein

Malaysian startup ento farms crickets in a controlled environment to develop nutritious cricket-based food products. The startup’s insect-based alternative protein offers more proteins per gram than beef and contains all nine essential amino acids.

Also, ento’s cricket farming solution requires less land, water, and food than traditional livestock production, reducing greenhouse gas emissions. This enables food producers to save production costs.

2. Nutraceuticals

There is an increasing concern about the impact of food habits on health and a growing need for essential nutrients for a healthy lifestyle. With the COVID-19 pandemic, consumers are focusing more on eating healthy, making nutraceuticals a top trend in the food industry. This is a critical element in driving the demand for nutraceuticals.

Nutriceuticals include nutritional supplements, functional foods, medicinal foods, and gut microbiome-enhancing foods like prebiotics, probiotics, and postbiotics. Scientific research on nutraceuticals underscores their potential health benefits. Importantly, their use in addressing oxidative stress-related disorders such as allergies, diabetes, and immune-related conditions.

Farmhand Organics provides Fermented Foods

Based in the US, FoodTech startup Farmhand Organics develops hand-crafted, naturally probiotic, plant-based, fermented foods using organic ingredients. The startup’s organic krauts and kimchi facilitate healthy digestion, as well as maintain sleep and immunity balance.

Further, Farmhand Organics’ fermented vegetables are nutrient-rich and flavorful. The startup sources its ingredients directly from family farms and other local suppliers, reducing the carbon footprint due to transport.

Voll Sante makes Functional Food

Indian startup Voll Sante provides functional foods and beverages as well as nutraceuticals. The startup’s products utilize natural ingredients to prevent and manage malnutrition. Its nutraceutical products assist consumers in preventing chronic diseases.

The startup’s nutraceuticals also reduce healthcare expenditures and turn the focus of healthcare from reactive to preventive. They are available as protein and vitamin supplements, immunity boosters, and breakfast cereals.

3. eCommerce

eCommerce has long been a focal point in the food and beverage industry, but the COVID-19 pandemic has accelerated innovations within food supply chains. Food brands are harnessing digital platforms to provide on-demand online delivery services and connect with customers through direct-to-customer (D2C) distribution models. Additionally, heightened safety concerns during the pandemic are fueling the growth of ghost kitchens or cloud kitchens, exclusively offering food takeout and delivery services.

In tandem with D2C strategies, brands are prioritizing omnichannel distribution to enhance the customer experience and boost sales. This integrated approach ensures that customers access products both online and in brick-and-mortar stores, catering to diverse shopping preferences. Further, eCommerce empowers food manufacturers to establish stronger connections with their customers while ensuring product availability. This direct engagement allows manufacturers to gather valuable feedback and data, enabling them to tailor their offerings to consumer preferences.

Byte Elephant builds a Food E-Commerce Platform

Byte Elephant is an Indian startup that develops BETs , an e-commerce platform for food companies. It allows multi-brand, multi-location businesses to manage all orders in a centralized ecosystem. The platform features a quick customer onboarding workflow and is customizable based on specific brand requirements.

The startup’s platform thus enables food businesses to mitigate upfront IT development costs and offer online service quickly. It also enables them to scale operations quickly. This allows food businesses to address customer demands and stay ahead of the competition.

Ghost Kitchen offers Kitchen Spaces

South Korean startup Ghost Kitchen provides individual kitchen spaces to quickly start delivery businesses. At present, the startup owns 143 kitchens with adequate hygiene management and workflow from material storage to cooking, packaging, and delivery.

Also, Ghost Kitchen enables data-driven planning services through shared data collection from the entire individual kitchens. In effect, the startup minimizes expenses for contracts and interiors by directly offering standalone kitchens for restaurants and other food delivery entrepreneurs.

4. Food Safety & Transparency

Consumers prioritize the quality and safety of food products, leading to heightened food safety concerns. To address these concerns, consumers now have access to smart labels and standalone food grading devices, empowering them to make informed decisions when selecting food items. Additionally, advancements in blockchain technology and real-time food monitoring via Internet of Things (IoT) devices allow food brands to offer comprehensive end-to-end traceability.

Further, startups are contributing to food safety and transparency by creating cost-effective and scalable monitoring solutions. These innovations foster trust between food producers and consumers, increasing brand credibility and sales. Startups also play a crucial role in driving continuous improvement in food safety practices and ensuring that consumers have access to reliable information about the products they consume.

Thrasos simplifies Cleaning Protocol Scanning

Thrasos is a French startup that offers an AI-based solution to scan cleaning protocols in food production lines. The startup’s food safety management system utilizes a deep learning network to analyze numerous food safety parameters to validate the compliance of hygiene procedures.

This approach enables food businesses to leverage food safety management as a service as well as monitor and assess the efficacy of cleaning operations. As a result, food companies are able to ensure food-safe production lines and improve customer safety.

ThisFish develops Seafood Traceability Software

ThisFish is a Canadian startup that develops Tally , a software solution for seafood traceability. The startup digitizes data collection in real time on the food manufacturing floor using electronic scales, barcode printers, and scanners. Also, Tally allows seafood manufacturers to upload catch certificates and compliance documents.

The startup’s approach strengthens traceability while eliminating data errors and building trust among customers. Furthermore, ThisFish assists seafood producers in streamlining their operations for overall cost reduction, from fleet management and port unloading to production and quality control.

5. Personalized Nutrition

Growing consumer awareness about nutrition is fueling the demand for personalized nutrition solutions . These solutions encompass nutrigenomics-based diets and also cater to individual preferences such as sugar-free, gluten-free, vegan diets, and clean-label food products. Technological advancements like 3D printing and the integration of robotics into food production lines enable food manufacturers to offer personalized nutrition on a large scale.

At-home blood and urine testing kits empower consumers to identify dietary choices that align with their genetic profiles. Tracking devices further allow users to monitor their diets and health conditions, streamlining their nutritional choices. This level of customization enhances customer convenience and drives sales. Furthermore, ongoing research and development efforts are continuously expanding the range of personalized nutrition offerings. This ensures that consumers have access to dietary options to suit their needs.

NGX provides Genetically Personalized Meal-Shakes

NGX is a British startup that develops genetically personalized meal shakes. The startup offers at-home DNA nutrition tests and utilizes the test results to personalize nutrition intake for each individual. NGX’s products are plant-based breakfast and snack shakes that contain 30 essential nutrients including valine, isoleucine, leucine, lysine, and more.

Also, the startup’s products have minimal calories, fat, and carbohydrate value as well as zero added sugars. NGX’s meal shakes enable customers to reach their fitness goals quicker and lead healthier lives.

Mefood Omics develops an Online Precision Nutrition Platform

Mefood Omics is a French startup that builds an online precision nutrition platform for nutrition professionals. It combines nutrigenetics, artificial intelligence, and machine learning to design personalized dietary plans and monitor patients. Patients use the platform to create appointments and track billing as well as support diet customization and dynamic patient tracking.

The company also provides a nutrigenetic test and syncs the results with patient profiles. This way, the platform integrates nutrigenetic information as the last layer of personalization in generating the diet plan. This allows nutrition professionals and patients to leverage data-driven eating habits tailored to patient-specific diet goals.

Find out how 10 emerging technologies shape your industry!

6. Restaurant Digitization

Digitization in restaurants enhances the customer experience and also streamlines operational management, leading to improved efficiency. It empowers restaurant brands to collect valuable data at every stage, facilitating data-driven decision-making across operations. The upheaval caused by the COVID-19 pandemic has accelerated the adoption of digital management systems throughout the food and beverage supply chain. To minimize direct human-to-human contact, restaurants are integrating digital menus, self-service kiosks, and cashless payment methods.

Additionally, emerging technologies like chatbots and voice bots are gaining prominence for assisting customers with food orders and addressing restaurant-related inquiries. Leveraging customer preference and behavior data, AI-enabled tools provide personalized food recommendations and even contribute to new recipe development. This transformation underscores the pivotal role of digital solutions in the evolving restaurant landscape.

AAHI develops a Customer Engagement Platform

AAHI is a Czech startup that provides a customer engagement platform for restaurants. The startup’s contactless ordering platform uses digital menus and QR codes, supports multiple languages, and is tailorable to brand specifications. This offers a unique interactive experience for customers along with minimizing direct contact between personnel and customers.

Also, the platform optimizes customer-restaurant interactions, thereby increasing order frequency. AAHI’s Ecological Marketplace further connects restaurants with customers interested in sustainability and promotes special offers. This allows businesses to turn unsold and less popular items into revenue, reducing food wastage.

RestaurantOS optimizes Restaurant Operations

RestaurantOS is a Canadian startup that creates a restaurant management platform. Its AI assistant automates back-office tasks like order creation, reservation management, menu pricing, and more. The platform also offers guest analytics and supports inventory management and customer loyalty programs.

Through its solution, RestaurantOS enables restaurant chains to optimize operations, save time, and cut costs. Its pay-as-you-go business model further alleviates upfront and per-order platform fees for food businesses.

7. Digital Food Management

The integration of big data analytics and AI, coupled with real-time monitoring, facilitates comprehensive digital food management from farm to fork. Innovative startups are developing food management solutions that optimize manufacturing processes and streamline supply chain operations. Further, the digitization of restaurants empowers businesses to analyze customer behavior and forecast demand based on consumer preferences.

Collectively, these digital solutions empower food producers to gain a deeper understanding of market requirements and anticipate potential disruptions. This minimizes losses and improves surplus food inventory management. For instance, the utilization of quantum computing allows startups to swiftly analyze critical disruptions, including pandemics, and simulate market fluctuations accurately. Additionally, customer and market intelligence enables brands to streamline marketing strategies and drive increased sales.

Tastewise develops a Food Intelligence AI Engine

Tastewise is an Israeli startup that provides customer intelligence. The startup’s food intelligence AI engine collects customer data from social media interactions and digital restaurant platforms. It then cleans and classifies the data using historical consumer behavior and restaurant activities.

Through this process, the platform identifies critical insights into customer preferences and overall trends. It visualizes these insights as dashboards and reports, enabling restaurant owners to formulate strategies for product innovation, content marketing, and customer retention.

Food Market Hub provides a Procurement and Inventory System

Food Market Hub , a Malaysian startup, provides a cloud-based procurement and inventory system for the food and beverage industry. It streamlines the supply chain by offering real-time inventory tracking and automated purchase order generation. It also emails the purchase orders to the suppliers.

Using its companion smartphone app, restaurant managers centralize all procurement activities in one accessible platform. Moreover, Food Market Hub enhances inventory control by leveraging data analysis from various sources, like weather reports and historical data, to make more accurate purchase forecasts.

8. Food Waste Reduction

Addressing food wastage is paramount in the global effort to combat food insecurity and reduce the environmental footprint associated with the food industry. Food entrepreneurs and major corporations are prioritizing food waste reduction as a means to achieve sustainability and cut operational costs. The implementation of advanced food monitoring solutions empowers food producers, restaurants, and smart cities to proactively mitigate food waste.

Further, there is a notable shift towards adopting zero-waste approaches in food manufacturing. This emphasizes the upcycling and reuse of food waste to create value and resonate with environmentally-conscious consumers. A prime example of this shift is the emergence of 3D food printing solutions that utilize food waste to produce edible food products. Such means effectively curb food waste at restaurants and other food outlets while promoting sustainability.

Lumitics develops a Smart Food Waste Tracker

Lumitics is a Singaporean startup that tracks food waste. The startup’s smart food waste tracker, Insight , uses proprietary AI-based image recognition technology. It offers chefs insights into food waste, fostering feedback loops that enhance food quality based on customer preferences.

This dynamic approach empowers restaurants and food establishments to optimize production and tailor menus to meet customer demands effectively. Lumitics’ Insight reduces food waste and costs in hotels, airplanes, cruise ships, and more.

Food Drop connects Surplus Food to Local Charities

British startup Food Drop connects stores with unsold or surplus food to local charities, schools, and community groups. The startup’s online platform allows stores and charities to sign up, which then matches the stores and local charities.

Food Drop’s automated platform ensures a supply of charities to collect the surplus from stores at the end of the day. Also, if a charity is unavailable for pick up, the platform notifies stores in real time. The startup’s solution provides monthly reports on donated food and carbon savings.

9. Robotics

In the food and beverage industry, robotics plays a pivotal role across the entire value chain. It improves efficiency, consistency, and scalability throughout food production processes. Hospitality robots find their place in hotels and restaurants, elevating customer convenience and safety. Robotic chefs and food processing robots further solidify food robotics as a prominent trend in food technology.

Autonomous drones and vehicles are emerging as efficient alternatives to manual delivery services, increasing cost savings. These drones and other food-handling robots facilitate rapid and cost-effective food tagging and monitoring in warehouses and grocery stores. In effect, the robotics bloom in the food industry is expediting food production revenue with enhanced speed and precise food quality control.

Bear Robotics manufactures Autonomous Food Service Robots

US-based startup Bear Robotics develops Servi , an autonomous food-serving robot. It complements waitstaff by delivering dishes and beverages in tight spaces using advanced camera and laser sensor navigation, allowing it to navigate without encountering blind spots.

By prioritizing safety, Servi promptly stops or adjusts its course when encountering obstacles. Bear Robotics’ solutions offer a transformative approach to hospitality, enhancing operational efficiency and service quality in the industry.

ROBOEATZ provides Autonomous Robotic Kitchens

ROBOEATZ is a Canadian startup developing AI-based autonomous robotic kitchens for restaurants, grocery stores, and office spaces. The startup’s ARK 03 robot uses proprietary software for autonomous operation of kitchen tasks, including hot and cold food storage, preparation, plating, cleaning, and sanitizing.

Additionally, ARK 03 features automatic ordering and inventory control that keeps a tab on ingredient supply and automatically requests refills from wholesalers. ROBOEATZ’s solution improves the efficiency of food production while requiring minimal human intervention and adhering to effective food safety practices.

10. 3D Food Printers

3D food printers open up possibilities for personalized diets and the creation of alternative protein-based meals, offering precision and consistency in nutrition. While material extrusion remains the most widely used method in food printing, startups are exploring innovative approaches such as laser and inkjet food printing, as well as bioprinting. These novel methods enhance the quality and accuracy of 3D-printed food products.

With an emphasis on meeting specific product requirements and ensuring consistent quality, ongoing research in 3D food printing is geared toward large-scale food production. As a result, 3D food printing simplifies production processes and reduces costs. Further, it empowers food brands to deliver personalized food products at scale without the need for additional tooling and operational expenses.

SavorEat prints Plant-based Meat

SavorEat is an Israeli startup that develops plant-based meat. The startup combines chef robots, proprietary 3D printing, and non-GMO ingredients to print meat. At present, the startup produces meatless burgers and plans to expand to steaks, kebabs, and alternative seafood.

SavorEat’s meat alternatives replicate the authentic taste, texture, and experience of real meat. They are also customizable to suit specific preferences, dietary needs, and lifestyles. This 3D printing technology ensures rapid, precise, and consistent results, making it ideal for food trucks, restaurants, and hotels.

COCUUS manufactures Bioinks for 3D Food Printers

Spanish startup COCUUS produces plant- or cell-based animal protein analogs using 2D/3D laser printing, bioprinting, and robotics. The startup offers LEVEL-UP inkjet 3D printers and LASERGLOW laser 2D/3D printers for food producers. The LASERGLOW is accurate to design specifications and supports 3D shapes as well as hyper-personalization.

On the other hand, LEVEL-UP is suitable for product customization with surface filling at scale. The startup’s 3D printers allow restaurants and food brands to personalize their products without tooling changes and help in improving the brand reach, boosting sales.

Discover all Food Tech Trends & Startups

The top 10 food technology trends drive sustainable food sources and food waste reduction, with Industry 4.0 technologies enhancing efficiency and scalability. 3D food printing emerges as a promising technology, complementing other key trends and prompting continued investment in 3D food printers and bio-inks. Furthermore, advances in AI and big data analytics play a crucial role in optimizing food industry operations.

In synergy, the food and beverage sector is making strides towards sustainability while elevating overall operational efficiency. The Food Industry Trends & Startups outlined in this report only scratch the surface of trends that we identified during our data-driven innovation & startup scouting process. Identifying new opportunities & emerging technologies to implement into your business goes a long way in gaining a competitive advantage.

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Kamala’s secret sauce: Working in fast food sets you up for success

Shona hendley, save articles for later.

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McPresident Kamala Harris? While I’m personally lovin’ the sound of that – despite Harris holding a part-time position at McDonald’s during college – I’m sure she’d prefer the title without the prefix.

If Harris gets the votes this November, she will become the first US president who has worked behind the McDonald’s counter, but she is far from the only successful person to do so. Pink, Pharrell Williams, Shania Twain and Jay Leno have also worn a fast-food uniform before making it big.

Amazon founder Jeff Bezos is another, often publicly reflecting on his first job flipping burgers. “You learn a lot as a teenager working at McDonald’s. It’s different from what you learn in school. Don’t underestimate the value of that,” he said in an interview with Wired .

This value, which I like to call the “secret sauce” of working in fast food, has served me well too.

Harris could become the first US president who has worked at McDonald’s. Credit: Bloomberg via Getty Images

As someone who donned my red, Colonel Sanders embroidered polo shirt, black slacks and matching cap to work behind the register of my local KFC for over five years through high school and into university, there’s no doubt that the skills I learnt, many mistakes I made and encounters I had – like those with “extra salt Sally”, a slightly aggressive regular who always demanded blood pressure rising levels of salt – set me up for my future working life.

As the fast food industry’s name suggests, it’s fast-paced and can be intense, especially for teenagers and younger people who, according to the Australian Bureau of Statistics, make up a significant portion of fast-food employees.

Yes, this can be challenging – impatient customers lining up all the way to the door is never a walk in the park – but it is also formative and greatly beneficial. I may not have Harris’ level of professional responsibility, but like her, I do (usually) remain calm under pressure. If you’re looking for someone who can salt chips while listening to a grown adult melt down over how long they’ve waited for a piece of chicken, you know, just the multitasker to call.

In addition to the fast-food industry’s inherent nature, HR and people strategist Sophie Firmanger says that many foundational skills, like teamwork and time management, lay the groundwork for success in virtually any career.

“Time management is a universal skill, essential for balancing responsibilities in your professional life and teamwork is the backbone of any successful organisation, and problem-solving is critical in leadership roles,” says Firmanger.

Skills like problem-solving and resilience can also be used outside of work, says clinical psychologist and author Dr Rebecca Ray.

“The ability to think on your feet and make quick decisions becomes a real asset when tackling everyday challenges, whether at home or in your personal life and facing demanding customers or hectic shifts builds emotional resilience, which helps in managing stress in other parts of life.”

And that is something I can vouch for. Many of the lessons I learnt working in fast food have proven surprisingly beneficial.

Every job has its shortcomings

No matter how good it sounds, the reality is, whether you’re president, pop star or a parent, there will be downsides to your job.

In my gig, this often involved cleaning the male toilet. This, along with the regular burns inflicted at the corn cob station, taught me very early on in my working life that there will always be tasks that you won’t enjoy doing and, in some cases, may literally scar you for life.

It also taught me that, unfortunately, you must still do them. (Or, in the case of cleaning the toilet, teach your kids how to do it.)

Working in the fast-food industry leaves you with an unexpected set of skills. Credit: iStock

People can be very difficult

The first rule of any customer service job is that the customer is always right. However, as anyone who has ever worked in customer service knows, this isn’t always the case. One former customer was adamant that our cook altered the Colonel’s Secret Herbs and Spices recipe just to annoy her after she complained about a batch of the chicken the week before.

Learning how to manage difficult people professionally and navigating conflict is one of the greatest skills that that job has ever taught me. Because no matter where you work, you will have to deal with tricky, sometimes unpleasant situations (and people) and need to resolve them without completely losing it.

You will work with all sorts of people

While I predominantly worked alongside other students, the team also included members from various age brackets and different cultural, religious, and socioeconomic backgrounds.

Away from the neon bucket of the restaurant, this group never would have mixed. Starting my career, understanding that teamwork is only possible if you embrace the differences of everyone in the team has made me a much more understanding person to work with.

Unexpected skills can become useful

And then there are the skills you never imagined you would use again after you’ve hung up your branded visor.

As I was learning the “first in, first out” system of food storage or how to make a Twister wrap that won’t fall apart, I assumed these skills would be useless outside the fast-food industry.

But fast-forward 20 years, and as I pack school lunches and pack my groceries away (old vegies at the top, new underneath) I realise these skills couldn’t be more valuable. You never know what skill you’re going to need when, only that you come away from these jobs knowing a lot more than when you started.

While it appears Harris has taken many similar learnings with her throughout her career – from McDonald’s to politics – I wonder whether the same could be said of Donald Trump.

As Harris’s running mate, Tim Walz, said: “Can you simply picture Donald Trump working at a McDonald’s, trying to make a McFlurry or something? He couldn’t run that damn McFlurry machine if it cost him anything.”

Now that’s fast food for thought.

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