modern agriculture technology essay

Agriculture’s connected future: How technology can yield new growth

The agriculture industry has radically transformed over the past 50 years. Advances in machinery have expanded the scale, speed, and productivity of farm equipment, leading to more efficient cultivation of more land. Seed, irrigation, and fertilizers also have vastly improved, helping farmers increase yields. Now, agriculture is in the early days of yet another revolution, at the heart of which lie data and connectivity. Artificial intelligence, analytics, connected sensors, and other emerging technologies could further increase yields, improve the efficiency of water and other inputs, and build sustainability and resilience across crop cultivation and animal husbandry.

The future of connectivity

As the world experiences a quantum leap in the speed and scope of digital connections, industries are gaining new and enhanced tools to boost productivity and spur innovation. Over the next decade, existing technologies like fiber, low-power wide-area networks (LPWAN), Wi-Fi 6, low- to mid-band 5G, and short-range connections like radio-frequency identification (RFID) will expand their reach as networks are built out and adoption grows. At the same time, new generations of these technologies will appear, with upgraded standards. In addition, new types of more revolutionary—and more capital-intensive—frontier connectivity, like high-band 5G and low-Earth-orbit (LEO) satellites, will begin to come online.

Together, these technological developments will unlock powerful new capabilities across industries. Near-global coverage will allow the expansion of use cases even to remote areas and will enable constant connectivity universally. Massive use of Internet of Things (IoT) applications and use cases will be enabled as new technologies allow very high device densities. And mission-critical services will take advantage of ultralow-latency, high-reliability, and high-security connections.

Without a solid connectivity infrastructure, however, none of this is possible. If connectivity is implemented successfully in agriculture, the industry could tack on $500 billion in additional value to the global gross domestic product by 2030, according to our research. This would amount to a 7 to 9 percent improvement from its expected total and would alleviate much of the present pressure on farmers. It is one of just seven sectors that, fueled by advanced connectivity, will contribute $2 trillion to $3 trillion in additional value to global GDP over the next decade, according to research by the McKinsey Center for Advanced Connectivity  and the McKinsey Global Institute  (MGI) (see sidebar “The future of connectivity”).

Demand for food is growing at the same time the supply side faces constraints in land and farming inputs. The world’s population is on track to reach 9.7 billion by 2050, 1 The World Population Prospects: 2015 Revision, United Nations, Department of Economic and Social Affairs, Population Division, 2015. requiring a corresponding 70 percent increase in calories available for consumption, even as the cost of the inputs needed to generate those calories is rising. 2 World Resources Report: Creating a Sustainable Food Future, United Nations, World Resources Institute, and the World Bank, 2013. By 2030, the water supply will fall 40 percent short of meeting global water needs, 3 World Could Face Water Availability Shortfall by 2030 if Current Trends Continue, Secretary-General Warns at Meeting of High-Level Panel, United Nations, 2016. and rising energy, labor, and nutrient costs are already pressuring profit margins. About one-quarter of arable land is degraded and needs significant restoration before it can again sustain crops at scale. 4 The State of the World’s Land and Water Resources for Food and Agriculture: Managing systems at risk, Food and Agriculture Organization of the United Nations and Earthscan, 2011. And then there are increasing environmental pressures, such as climate change and the economic impact of catastrophic weather events, and social pressures, including the push for more ethical and sustainable farm practices, such as higher standards for farm-animal welfare and reduced use of chemicals and water.

To address these forces poised to further roil the industry, agriculture must embrace a digital transformation enabled by connectivity. Yet agriculture remains less digitized compared with many other industries globally. Past advances were mostly mechanical, in the form of more powerful and efficient machinery, and genetic, in the form of more productive seed and fertilizers. Now much more sophisticated, digital tools are needed to deliver the next productivity leap. Some already exist to help farmers more efficiently and sustainably use resources, while more advanced ones are in development. These new technologies can upgrade decision making, allowing better risk and variability management to optimize yields and improve economics. Deployed in animal husbandry, they can enhance the well-being of livestock, addressing the growing concerns over animal welfare.

Demand for food is growing at the same time the supply side faces constraints in land and farming inputs.

But the industry confronts two significant obstacles. Some regions lack the necessary connectivity infrastructure, making development of it paramount. In regions that already have a connectivity infrastructure, farms have been slow to deploy digital tools because their impact has not been sufficiently proven.

The COVID-19 crisis has further intensified other challenges agriculture faces in five areas: efficiency, resilience, digitization, agility, and sustainability. Lower sales volumes have pressured margins, exacerbating the need for farmers to contain costs further. Gridlocked global supply chains have highlighted the importance of having more local providers, which could increase the resilience of smaller farms. In this global pandemic, heavy reliance on manual labor has further affected farms whose workforces face mobility restrictions. Additionally, significant environmental benefits from decreased travel and consumption during the crisis are likely to drive a desire for more local, sustainable sourcing, requiring producers to adjust long-standing practices. In short, the crisis has accentuated the necessity of more widespread digitization and automation, while suddenly shifting demand and sales channels have underscored the value of agile adaptation.

Current connectivity in agriculture

In recent years, many farmers have begun to consult data about essential variables like soil, crops, livestock, and weather. Yet few if any have had access to advanced digital tools that would help to turn these data into valuable, actionable insights. In less-developed regions, almost all farmwork is manual, involving little or no advanced connectivity or equipment.

Even in the United States, a pioneer country in connectivity, only about one-quarter of farms currently use any connected equipment or devices to access data, and that technology isn’t exactly state-of-the-art, running on 2G or 3G networks that telcos plan to dismantle or on very low-band IoT networks that are complicated and expensive to set up. In either case, those networks can support only a limited number of devices and lack the performance for real-time data transfer, which is essential to unlock the value of more advanced and complex use cases.

Nonetheless, current IoT technologies running on 3G and 4G cellular networks are in many cases sufficient to enable simpler use cases, such as advanced monitoring of crops and livestock. In the past, however, the cost of hardware was high, so the business case for implementing IoT in farming did not hold up. Today, device and hardware costs are dropping rapidly, and several providers now offer solutions at a price we believe will deliver a return in the first year of investment.

These simpler tools are not enough, though, to unlock all the potential value that connectivity holds for agriculture. To attain that, the industry must make full use of digital applications and analytics, which will require low latency, high bandwidth, high resiliency, and support for a density of devices offered by advanced and frontier connectivity technologies like LPWAN, 5G, and LEO satellites (Exhibit 1).

The challenge the industry is facing is thus twofold: infrastructure must be developed to enable the use of connectivity in farming, and where connectivity already exists, strong business cases must be made in order for solutions to be adopted. The good news is that connectivity coverage is increasing almost everywhere. By 2030, we expect advanced connectivity infrastructure of some type to cover roughly 80 percent of the world’s rural areas; the notable exception is Africa, where only a quarter of its area will be covered. The key, then, is to develop more—and more effective—digital tools for the industry and to foster widespread adoption of them.

As connectivity increasingly takes hold, these tools will enable new capabilities in agriculture:

• Massive Internet of Things. Low-power networks and cheaper sensors will set the stage for the IoT to scale up, enabling such use cases as precision irrigation of field crops, monitoring of large herds of livestock, and tracking of the use and performance of remote buildings and large fleets of machinery.
• Mission-critical services. Ultralow latency and improved stability of connections will foster confidence to run applications that demand absolute reliability and responsiveness, such as operating autonomous machinery and drones.
• Near-global coverage. If LEO satellites attain their potential, they will enable even the most remote rural areas of the world to use extensive digitization, which will enhance global farming productivity.

Connectivity’s potential for value creation

By the end of the decade, enhanced connectivity in agriculture could add more than $500 billion to global gross domestic product, a critical productivity improvement of 7 to 9 percent for the industry. 5 This represents our estimate of the total potential for value added in agricultural production; it is not an estimate of the agritech and precision-agriculture market size. Much of that value, however, will require investments in connectivity that today are largely absent from agriculture. Other industries already use technologies like LPWAN, cloud computing, and cheaper, better sensors requiring minimal hardware, which can significantly reduce the necessary investment. We have analyzed five use cases—crop monitoring, livestock monitoring, building and equipment management, drone farming, and autonomous farming machinery—where enhanced connectivity is already in the early stages of being used and is most likely to deliver the higher yields, lower costs, and greater resilience and sustainability that the industry needs to thrive in the 21st century (Exhibit 2).

It’s important to note that use cases do not apply equally across regions. For example, in North America, where yields are already fairly optimized, monitoring solutions do not have the same potential for value creation as in Asia or Africa, where there is much more room to improve productivity. Drones and autonomous machinery will deliver more impact to advanced markets, as technology will likely be more readily available there (Exhibit 3).

About the use-case research

The value of our agriculture-connectivity use cases resides primarily in labor efficiencies, input optimization, yield increases, reduced overhead, and improvements in operation and maintenance of machinery. Each use case enables a series of improvement levers in those areas that promise to enhance the productivity of farming (exhibit).

We applied those levers to the profitability drivers of agricultural production to derive an economic potential for the industry as a whole. For example, a use case might enable a 5 to 10 percent reduction in fertilizer usage, saving costs for the farmer, or enable 3 percent higher yields, leading to greater revenues for the farmer. In fact, higher yields represent the largest opportunity, with advanced connectivity potentially adding some $350 billion of value to global food production without additional inputs or labor costs.

Potential value initially will accrue to large farms that have more investing power and better incentives to digitize. Connectivity promises easier surveying of large tracts, and the fixed costs of developing IoT solutions are more easily offset in large production facilities than on small family farms. Crops like cereals, grains, fruits, and vegetables will generate most of the value we identified, for similar reasons. Connectivity enables more use cases in these sectors than in meat and dairy, because of the large average size of farms, relatively higher player consolidation, and better applicability of connected technologies, as IoT networks are especially adapted to static monitoring of many variables. It’s also interesting to note that Asia should garner about 60 percent of the total value simply because it produces the biggest volume of crops (see sidebar “About the use-case research”).

Use case 1: Crop monitoring

Connectivity offers a variety of ways to improve the observation and care of crops. Integrating weather data, irrigation, nutrient, and other systems could improve resource use and boost yields by more accurately identifying and predicting deficiencies. For instance, sensors deployed to monitor soil conditions could communicate via LPWAN, directing sprinklers to adjust water and nutrient application. Sensors could also deliver imagery from remote corners of fields to assist farmers in making more informed and timely decisions and getting early warnings of problems like disease or pests.

Smart monitoring could also help farmers optimize the harvesting window. Monitoring crops for quality characteristics—say, sugar content and fruit color—could help farmers maximize the revenue from their crops.

Most IoT networks today cannot support imagery transfer between devices, let alone autonomous imagery analysis, nor can they support high enough device numbers and density to monitor large fields accurately. Narrowband Internet of Things (NB-IoT) and 5G promise to solve these bandwidth and connection-density issues. The use of more and smoother connections between soil, farm equipment, and farm managers could unlock $130 billion to $175 billion in value by 2030.

Use case 2: Livestock monitoring

Preventing disease outbreaks and spotting animals in distress are critical in large-scale livestock management, where most animals are raised in close quarters on a regimen that ensures they move easily through a highly automated processing system. Chips and body sensors that measure temperature, pulse, and blood pressure, among other indicators, could detect illnesses early, preventing herd infection and improving food quality. Farmers are already using ear-tag technology from providers such as Smartbow (part of Zoetis) to monitor cows’ heat, health, and location, or technology from companies such as Allflex to implement comprehensive electronic tracing in case of disease outbreaks.

Similarly, environmental sensors could trigger automatic adjustments in ventilation or heating in barns, lessening distress and improving living conditions that increasingly concern consumers. Better monitoring of animal health and growth conditions could produce $70 billion to $90 billion in value by 2030.

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Use case 3: building and equipment management.

Chips and sensors to monitor and measure levels of silos and warehouses could trigger automated reordering, reducing inventory costs for farmers, many of whom are already using such systems from companies like Blue Level Technologies. Similar tools could also improve shelf life of inputs and reduce post-harvest losses by monitoring and automatically optimizing storage conditions. Monitoring conditions and usage of buildings and equipment also has the potential to reduce energy consumption. Computer vision and sensors attached to equipment and connected to predictive-maintenance systems could decrease repair costs and extend machinery and equipment life.

Such solutions could achieve $40 billion to $60 billion in cost savings by 2030.

Use case 4: Farming by drone

Agriculture has been using drones for some two decades, with farmers around the world relying on pioneers like Yamaha’s RMAX remote-controlled helicopter to help with crop spraying. Now the next generation of drones is starting to impact the sector, with the ability to survey crops and herds over vast areas quickly and efficiently or as a relay system for ferrying real-time data to other connected equipment and installations. Drones also could use computer vision to analyze field conditions and deliver precise interventions like fertilizers, nutrients, and pesticides where crops most need them. Or they could plant seed in remote locations, lowering equipment and workforce costs. By reducing costs and improving yields, the use of drones could generate between $85 billion and $115 billion in value.

Use case 5: Autonomous farming machinery

More precise GPS controls paired with computer vision and sensors could advance the deployment of smart and autonomous farm machinery. Farmers could operate a variety of equipment on their field simultaneously and without human intervention, freeing up time and other resources. Autonomous machines are also more efficient and precise at working a field than human-operated ones, which could generate fuel savings and higher yields. Increasing the autonomy of machinery through better connectivity could create $50 billion to $60 billion of additional value by 2030.

Additional sources of value

Connected technologies offer an additional, indirect benefit, the value of which is not included in the estimates given in these use cases. The global farming industry is highly fragmented, with most labor done by individual farm owners. Particularly in Asia and Africa, few farms employ outside workers. On such farms, the adoption of connectivity solutions should free significant time for farmers, which they can use to farm additional land for pay or to pursue work outside the industry.

We find the value of deploying advanced connectivity on these farms to achieve such labor efficiencies represents almost $120 billion, bringing the total value of enhanced connectivity from direct and indirect outcomes to more than $620 billion by 2030. The extent to which this value will be captured, however, relies largely on advanced connectivity coverage, which is expected to be fairly low, around 25 percent, in Africa and poorer parts of Asia and Latin America. Achieving the critical mass of adopters needed to make a business case for deploying advanced connectivity also will be more difficult in those regions, where farming is more fragmented than in North America and Europe.

Connected world: An evolution in connectivity beyond the 5G revolution

Implications for the agricultural ecosystem.

As the agriculture industry digitizes, new pockets of value will likely be unlocked. To date, input providers selling seed, nutrients, pesticides, and equipment have played a critical role in the data ecosystem because of their close ties with farmers, their own knowledge of agronomy, and their track record of innovation. For example, one of the world’s largest fertilizer distributors now offers both fertilizing agents and software that analyzes field data to help farmers determine where to apply their fertilizers and in what quantity. Similarly, a large-equipment manufacturer is developing precision controls that make use of satellite imagery and vehicle-to-vehicle connections to improve the efficiency of field equipment.

Advanced connectivity does, however, give new players an opportunity to enter the space. For one thing, telcos and LPWAN providers have an essential role to play in installing the connectivity infrastructure needed to enable digital applications on farms. They could partner with public authorities and other agriculture players to develop public or private rural networks, capturing some of the new value in the process.

Agritech companies are another example of the new players coming into the agriculture sphere. They specialize in offering farmers innovative products that make use of technology and data to improve decision making and thereby increase yields and profits. Such agritech enterprises could proffer solutions and pricing models that reduce perceived risk for farmers—with, for example, subscription models that remove the initial investment burden and allow farmers to opt out at any time—likely leading to faster adoption of their products. An Italian agritech is doing this by offering to monitor irrigation and crop protection for wineries at a seasonal, per-acre fee inclusive of hardware installation, data collection and analysis, and decision support. Agritech also could partner with agribusinesses to develop solutions.

Still, much of this cannot happen until many rural areas get access to a high-speed broadband network. We envision three principal ways the necessary investment could take place to make this a reality:

• Telco-driven deployment. Though the economics of high-bandwidth rural networks have generally been poor, telcos could benefit from a sharp increase in rural demand for their bandwidth as farmers embrace advanced applications and integrated solutions.
• Provider-driven deployment. Input providers, with their existing industry knowledge and relationships, are probably best positioned to take the lead in connectivity-related investment. They could partner with telcos or LPWAN businesses to develop rural connectivity networks and then offer farmers business models integrating connected technology and product and decision support.
• Farmer-driven deployment. Farm owners, alone or in tandem with LPWAN groups or telcos, could also drive investment. This would require farmers to develop the knowledge and skills to gather and analyze data locally, rather than through third parties, which is no small hurdle. But farmers would retain more control over data.

How to do it

Regardless of which group drives the necessary investment for connectivity in agriculture, no single entity will be able to go it alone. All of these advances will require the industry’s main actors to embrace collaboration as an essential aspect of doing business. Going forward, winners in delivering connectivity to agriculture will need deep capabilities across various domains, ranging from knowledge of farm operations to advanced data analytics and the ability to offer solutions that integrate easily and smoothly with other platforms and adjacent industries. For example, data gathered by autonomous tractors should seamlessly flow to the computer controlling irrigation devices, which in turn should be able to use weather-station data to optimize irrigation plans.

Connectivity pioneers in the industry, however, have already started developing these new capabilities internally. Organizations prefer keeping proprietary data on operations internal for confidentiality and competitive reasons. This level of control also makes the data easier to analyze and helps the organization be more responsive to evolving client needs.

But developing new capabilities is not the end game. Agriculture players able to develop partnerships with telcos or LPWAN players will gain significant leverage in the new connected-agriculture ecosystem. Not only will they be able to procure connectivity hardware more easily and affordably through those partnerships, they will also be better positioned to develop close relationships with farmers as connectivity becomes a strategic issue. Input providers or distributors could thus find themselves in a connectivity race. If input providers manage to develop such partnerships, they could connect directly with farmers and cut out distributors entirely. If distributors win that race, they will consolidate their position in the value chain by remaining an essential intermediary, closer to the needs of farmers.

The public sector also could play a role by improving the economics of developing broadband networks, particularly in rural areas. For example, the German and Korean governments have played a major role in making network development more attractive by heavily subsidizing spectrum or providing tax breaks to telcos. 6 “Das Breitbandförderprogramm des Bundes” [in German], Bundesministerium für Verkehr und digitale Infrastruktur, 2020, bmvi.de; 5G in Korea: Volume 1: Get a taste of the future, Samsung Electronics, 2019, samsungnetworks.com. Other regions could replicate this model, accelerating development of connective products by cost-effectively giving input providers and agritech companies assurance of a backbone over which they could deliver services. Eventual deployment of LEO satellite constellations would likely have a similar impact.

Agriculture, one of the world’s oldest industries, finds itself at a technological crossroads. To handle increasing demand and several disruptive trends successfully, the industry will need to overcome the challenges to deploying advanced connectivity. This will require significant investment in infrastructure and a realignment of traditional roles. It is a huge but critical undertaking, with more than $500 billion in value at stake. The success and sustainability of one of the planet’s oldest industries may well depend on this technology transformation, and those that embrace it at the outset may be best positioned to thrive in agriculture’s connectivity-driven future.

Lutz Goedde is a senior partner and global leader of McKinsey’s Agriculture Practice in the Denver office; Joshua Katz is a partner in the Stamford office; and Alexandre Menard is a senior partner in the Paris office, where Julien Revellat is an associate partner.

The authors wish to thank Nicolas D. Estais, Claus Gerckens, Vincent Tourangeau, and the McKinsey Center for Advanced Connectivity for their contributions to the article.

This article was edited by Daniel Eisenberg, a senior editor in the New York office.

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• Published: 27 April 2017

Technology: The Future of Agriculture

• Anthony King  

Nature volume  544 ,  pages S21–S23 ( 2017 ) Cite this article

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• Agriculture

A technological revolution in farming led by advances in robotics and sensing technologies looks set to disrupt modern practice.

Over the centuries, as farmers have adopted more technology in their pursuit of greater yields, the belief that 'bigger is better' has come to dominate farming, rendering small-scale operations impractical. But advances in robotics and sensing technologies are threatening to disrupt today's agribusiness model. “There is the potential for intelligent robots to change the economic model of farming so that it becomes feasible to be a small producer again,” says robotics engineer George Kantor at Carnegie Mellon University in Pittsburgh, Pennsylvania.

Twenty-first century robotics and sensing technologies have the potential to solve problems as old as farming itself. “I believe, by moving to a robotic agricultural system, we can make crop production significantly more efficient and more sustainable,” says Simon Blackmore, an engineer at Harper Adams University in Newport, UK. In greenhouses devoted to fruit and vegetable production, engineers are exploring automation as a way to reduce costs and boost quality (see ‘ Ripe for the picking ’). Devices to monitor vegetable growth, as well as robotic pickers, are currently being tested. For livestock farmers, sensing technologies can help to manage the health and welfare of their animals (‘ Animal trackers ’). And work is underway to improve monitoring and maintenance of soil quality (‘ Silicon soil saviours ’), and to eliminate pests and disease without resorting to indiscriminate use of agrichemicals (‘ Eliminating enemies ’).

Although some of these technologies are already available, most are at the research stage in labs and spin-off companies. “Big-machinery manufacturers are not putting their money into manufacturing agricultural robots because it goes against their current business models,” says Blackmore. Researchers such as Blackmore and Kantor are part of a growing body of scientists with plans to revolutionize agricultural practice. If they succeed, they'll change how we produce food forever. “We can use technology to double food production,” says Richard Green, agricultural engineer at Harper Adams.

Ripe for the picking

The Netherlands is famed for the efficiency of its fruit- and vegetable-growing greenhouses, but these operations rely on people to pick the produce. “Humans are still better than robots, but there is a lot of effort going into automatic harvesting,” says Eldert van Henten, an agricultural engineer at Wageningen University in the Netherlands, who is working on a sweet-pepper harvester. The challenge is to quickly and precisely identify the pepper and avoid cutting the main stem of the plant. The key lies in fast, precise software. “We are performing deep learning with the machine so it can interpret all the data from a colour camera fast,” says van Henten. “We even feed data from regular street scenes into the neural network to better train it.”

In the United Kingdom, Green has developed a strawberry harvester that he says can pick the fruit faster than humans. It relies on stereoscopic vision with RGB cameras to capture depth, but it is its powerful algorithms that allow it to pick a strawberry every two seconds. People can pick 15 to 20 a minute, Green estimates. “Our partners at the National Physical Laboratory worked on the problem for two years, but had a brainstorm one day and finally cracked it,” says Green, adding that the solution is too commercially sensitive to share. He thinks that supervised groups of robots can step into the shoes of strawberry pickers in around five years. Harper Adams University is considering setting up a spin-off company to commercialize the technology. The big hurdle to commercialization, however, is that food producers demand robots that can pick all kinds of vegetables, says van Henten. The variety of shapes, sizes and colours of tomatoes, for instance, makes picking them a tough challenge, although there is already a robot available to remove unwanted leaves from the plants.

Another key place to look for efficiencies is timing. Picking too early is wasteful because you miss out on growth, but picking too late slashes weeks off the storage time. Precision-farming engineer Manuela Zude-Sasse at the Leibniz Institute for Agricultural Engineering and Bioeconomy in Potsdam, Germany, is attaching sensors to apples to detect their size, and levels of the pigments chlorophyll and anthocyanin. The data are fed into an algorithm to calculate developmental stage, and, when the time is ripe for picking, growers are alerted by smartphone.

So far, Zude-Sasse has put sensors on pears, citrus fruits, peaches, bananas and apples ( pictured ). She is set to start field trials later this year in a commercial tomato greenhouse and an apple orchard. She is also developing a smartphone app for cherry growers. The app will use photographs of cherries taken by growers to calculate growth rate and a quality score.

Growing fresh fruit and vegetables is all about keeping the quality high while minimizing costs. “If you can schedule harvest to optimum fruit development, then you can reap an economic benefit and a quality one,” says Zude-Sasse.

Eliminating enemies

The Food and Agriculture Organization of the United Nations estimates that 20–40% of global crop yields are lost each year to pests and diseases, despite the application of around two-million tonnes of pesticide. Intelligent devices, such as robots and drones, could allow farmers to slash agrichemical use by spotting crop enemies earlier to allow precise chemical application or pest removal, for example. “The market is demanding foods with less herbicide and pesticide, and with greater quality,” says Red Whittaker, a robotics engineer at Carnegie Mellon who designed and patented an automated guidance system for tractors in 1997. “That challenge can be met by robots.”

“We predict drones, mounted with RGB or multispectral cameras, will take off every morning before the farmer gets up, and identify where within the field there is a pest or a problem,” says Green. As well as visible light, these cameras would be able to collect data from the invisible parts of the electromagnetic spectrum that could allow farmers to pinpoint a fungal disease, for example, before it becomes established. Scientists from Carnegie Mellon have begun to test the theory in sorghum ( Sorghum bicolor ), a staple in many parts of Africa and a potential biofuel crop in the United States.

Agribotix, an agriculture data-analysis company in Boulder, Colorado, supplies drones and software that use near-infrared images to map patches of unhealthy vegetation in large fields. Images can also reveal potential causes, such as pests or problems with irrigation. The company processes drone data from crop fields in more than 50 countries. It is now using machine learning to train its systems to differentiate between crops and weeds, and hopes to have this capability ready for the 2017 growing season. “We will be able to ping growers with an alert saying you have weeds growing in your field, here and here,” says crop scientist Jason Barton, an executive at Agribotix.

Modern technology that can autonomously eliminate pests and target agrichemicals better will reduce collateral damage to wildlife, lower resistance and cut costs. “We are working with a pesticide company keen to apply from the air using a drone,” says Green. Rather than spraying a whole field, the pesticide could be delivered to the right spot in the quantity needed, he says. The potential reductions in pesticide use are impressive. According to researchers at the University of Sydney's Australian Centre for Field Robotics, targeted spraying of vegetables used 0.1% of the volume of herbicide used in conventional blanket spraying. Their prototype robot is called RIPPA (Robot for Intelligent Perception and Precision Application) and shoots weeds with a directed micro-dose of liquid. Scientists at Harper Adams are going even further, testing a robot that does away with chemicals altogether by blasting weeds close to crops with a laser. “Cameras identify the growing point of the weed and our laser, which is no more than a concentrated heat source, heats it up to 95 °C, so the weed either dies or goes dormant,” says Blackmore.

Animal trackers

Smart collars — a bit like the wearable devices designed to track human health and fitness — have been used to monitor cows in Scotland since 2010. Developed by Glasgow start-up Silent Herdsman, the collar monitors fertility by tracking activity — cows move around more when they are fertile — and uses this to alert farmers to when a cow is ready to mate, sending a message to his or her laptop or smartphone. The collars ( pictured ), which are now being developed by Israeli dairy-farm-technology company Afimilk after they acquired Silent Herdsman last year, also detect early signs of illness by monitoring the average time each cow spends eating and ruminating, and warning the farmer via a smartphone if either declines.

“We are now looking at more subtle behavioural changes and how they might be related to animal health, such as lameness or acidosis,” says Richard Dewhurst, an animal nutritionist at Scotland's Rural College (SRUC) in Edinburgh, who is involved in research to expand the capabilities of the collar. Scientists are developing algorithms to interrogate data collected by the collars.

In a separate project, Dewhurst is analysing levels of exhaled ketones and sulfides in cow breath to reveal underfeeding and tissue breakdown or excess protein in their diet. “We have used selected-ionflow-tube mass spectrometry, but there are commercial sensors available,” says Dewhurst.

Cameras are also improving the detection of threats to cow health. The inflammatory condition mastitis — often the result of a bacterial infection — is one of the biggest costs to the dairy industry, causing declines in milk production or even death. Thermal-imaging cameras installed in cow sheds can spot hot, inflamed udders, allowing animals to be treated early.

Carol-Anne Duthie, an animal scientist at SRUC, is using 3D cameras to film cattle at water troughs to estimate the carcass grade (an assessment of the quality of a culled cow) and animal weight. These criteria determine the price producers are paid. Knowing the optimum time to sell would maximize profit and provide abattoirs with more-consistent animals. “This has knock on effects in terms of overall efficiency of the entire supply chain, reducing the animals which are out of specification reaching the abattoir,” Duthie explains.

And researchers in Belgium have developed a camera system to monitor broiler chickens in sheds. Three cameras continually track the movements of thousands of individual birds to spot problems quickly. “Analysing the behaviour of broilers can give an early warning for over 90% of problems,” says bioengineer Daniel Berckmans at the University of Leuven. The behaviour-monitoring system is being sold by Fancom, a livestock-husbandry firm in Panningen, the Netherlands. The Leuven researchers have also launched a cough monitor to flag respiratory problems in pigs, through a spin-off company called SoundTalks. This can give a warning 12 days earlier than farmers or vets would normally be able to detect a problem, says Berckmans. The microphone, which is positioned above animals in their pen, identifies sick individuals so that treatment can be targeted. “The idea was to reduce the use of antibiotics,” says Berckmans.

Berckmans is now working on downsizing a stress monitor designed for people so that it will attach to a cow's ear tag. “The more you stress an animal, the less energy is available from food for growth,” he says. The monitor takes 200 physiological measurements a second, alerting farmers through a smartphone when there is a problem.

Silicon soil saviours

The richest resource for arable farmers is soil. But large harvesters damage and compact soil, and overuse of agrichemicals such as nitrogen fertilizer are bad for both the environment and a farmer's bottom line. Robotics and autonomous machines could help.

Data from drones are being used for smarter application of nitrogen fertilizer. “Healthy vegetation reflects more near-infrared light than unhealthy vegetation,” explains Barton. The ratio of red to near-infrared bands on a multispectral image can be used to estimate chlorophyll concentration and, therefore, to map biomass and see where interventions such as fertilization are needed after weather or pest damage, for example. When French agricultural technology company Airinov, which offers this type of drone survey, partnered with a French farming cooperative, they found that over a period of 3 years, in 627 fields of oilseed rape ( Brassica napus ), farmers used on average 34 kilograms less nitrogen fertilizer per hectare than they would without the survey data. This saved on average €107 (US$115) per hectare per year.

Bonirob ( pictured ) — a car-sized robot originally developed by a team of scientists including those at Osnabrück University of Applied Sciences in Germany — can measure other indicators of soil quality using various sensors and modules, including a moisture sensor and a penetrometer, which is used to assess soil compaction. According to Arno Ruckelshausen, an agricultural technologist at Osnabrück, Bonirob can take a sample of soil, liquidize it and analyse it to precisely map in real time characteristics such as pH and phosphorous levels. The University of Sydney's smaller RIPPA robot can also detect soil characteristics that affect crop production, by measuring soil conductivity.

Soil mapping opens the door to sowing different crop varieties in one field to better match shifting soil properties such as water availability. “You could differentially seed a field, for example, planting deep-rooting barley or wheat varieties in more sandy parts,” says Maurice Moloney, chief executive of the Global Institute for Food Security in Saskatoon, Canada. Growing multiple crops together could also lead to smarter use of agrichemicals. “Nature is strongly against monoculture, which is one reason we have to use massive amounts of herbicide and pesticides,” says van Henten. “It is about making the best use of resources.”

Mixed sowing would challenge an accepted pillar of agricultural wisdom: that economies of scale and the bulkiness of farm machinery mean vast fields of a single crop is the most-efficient way to farm, and the bigger the machine, the more-efficient the process. Some of the heaviest harvesters weigh 60 tonnes, cost more than a top-end sports car and leave a trail of soil compaction in their wake that can last for years.

But if there is no need for the farmer to drive the machine, then one large vehicle that covers as much area as possible is no longer needed. “As soon as you remove the human component, size is irrelevant,” says van Henten. Small, autonomous robots make mixed planting feasible and would not crush the soil.

In April, researchers at Harpers Adams began a proof-of-concept experiment with a hectare of barley. “We plan to grow and harvest the entire crop from start to finish with no humans entering the field,” says Green. The experiment will use existing machinery, such as tractors, that have been made autonomous, rather than new robots, but their goal is to use the software developed during this trial as the brains of purpose-built robots in the future. “Robots can facilitate a new way of doing agriculture,” says van Henten. Many of these disruptive technologies may not be ready for the prime time just yet, but the revolution is coming.

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King, A. Technology: The Future of Agriculture. Nature 544 , S21–S23 (2017). https://doi.org/10.1038/544S21a

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Impact of Technology on Agriculture

Technological innovations have greatly shaped agriculture throughout time. From the creation of the plow to the global positioning system (GPS) driven precision farming equipment, humans have developed new ways to make farming more efficient and grow more food. We are constantly working to find new ways to irrigate crops or breed more disease resistant varieties. These iterations are key to feeding the ever-expanding global population with the decreasing freshwater supply.

Explore developments in agricultural technology and its impacts on civilization with this curated collection of classroom resources.

Earth Science, Geography

Here's how technology is helping solve agriculture's biggest issues

Technology can help farmers improve food security sustainably. Image:  Shutterstock

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• The current ways of producing and consuming food have led to many seemingly intractable issues that threaten the planet and our food security.

With a global agri-food system that needs to adapt to the impacts of climate change, as well as feed a growing population of over 8.2 billion people, it’s clear that the way we produce, distribute and consume food must change.

• The World Economic Forum has launched the Food Innovation Hubs to drive relevant high-end and low-cost inclusive innovation that could have a scalable impact.

Technology in agriculture is nothing new. Civilizations have been developing tools and methods to cultivate the land and domesticate animals ever since the nomadic lifestyle was left by the wayside. The current ways of producing and consuming food, however, have led to many seemingly intractable issues that threaten the planet and risk our ability to feed ourselves in the future.

The agriculture sector is the second largest global emitter of greenhouse gas emissions, second only to the energy sector. It is also responsible for 70% of all freshwater withdrawals globally. Overproduction and unsustainable farming practices, further exacerbated by climate change, are degrading our soil. Today, over half of agricultural land is degraded, resulting in productivity losses of $400 billion per year . The backbone of this system is smallholder farms that produce 29% of the world’s crops .

Here, are a few technology solutions offering hope that pave the way towards net-zero and nature-friendly and water-positive agri-food practices.

The Forum’s Artificial Intelligence for Agriculture Innovation (AI4AI) initiative aims to transform the agriculture sector using AI and other cutting-edge technologies.

Led by the Forum’s Centre for the Fourth Industrial Revolution (C4IR) in India, the initiative has already helped more than 7000 chilli farmers increase their yields and reduce costs.

C4IR India is sharing its learnings with other centres in the C4IR Global Network, including Saudi Arabia, South Africa and Colombia.

A whopping 14.5% of global emissions come from the animal agriculture industry , with 65% of these emissions attributed to beef and dairy cattle. Investors are now seeing this as an opportunity to focus on protein innovation. In 2022, investments in this space in Europe have increased by 24% , with companies raising €579 million ($622 million). However, to ensure a protein transformation that can secure livelihoods, nutrition and the environment, a multiplicity of transition pathways are needed - innovations that can support sustainable intensification, drive fit-for-purpose diversification and support consumer needs.

Precision fermentation delivers additional solutions to conventional dairy production methods, not only demonstrating the potential of protein innovation, but also contributing to the reduction of environmental impact. The animal-free dairy proteins producer Remilk received regulatory approval for its products from Israel’s Ministry of Health, as well as the Singapore Food Authority . After a successful raise of $120 million, Remilk is embarking on building what might be the largest cow-free milk production facility.

Although technological progress is being made, the transformation of the sector is complex and the cost of innovation is too high. To achieve that progress, we will need coordinated action, targeted policies and investments, and, most importantly, to build trust to ensure that technology facilitates the equitable and sustainable transition of the protein sector, while mitigating unintended consequences.

Freshwater use

As each year brings news of worsening drought conditions , the case for the reduction in freshwater use has never been stronger. Innovation can play a large part in changing our agricultural practices that deplete our freshwater supplies, from precision agriculture that can pinpoint the precise amount of irrigation required, to advances in biotechnologies that can create more drought-tolerant crops.

Kilimo is leveraging the power of big data and machine learning to verify, improve and offset water usage in agriculture. The unique power of this software-as-a-service solution is that it allows farmers to sell water offsets to companies that want to become water-neutral. In monitoring over 370,000 acres across Latin America, over 72 billion litres of water have been saved so far.

Soil degradation

Technologies have a role to play in scaling climate and nature-friendly agricultural practices to safeguard and restore the health of our soils, while contributing to the fight against climate change. The key to making an impact in this area is closing the soil health knowledge gap, often through the use of technologies and data.

Cutting-edge companies, such as Boomitra , are deploying satellite and AI technology to enable the monitoring, reporting and verification of carbon removal credits across the globe. Boomitra is working with over 150,000 farmers and ranchers managing five million acres of land across Africa, South America and Asia. Its platform can measure soil improvements and the soil’s ability to store carbon over time, allowing those third-party verified carbon credits to be sold on the marketplace, with a majority of the revenue generated going directly to the farmer, rancher or landowner. This innovative model has been so successful Boomitra was named as a 2023 finalist for the Earthshot Prize .

Have you read?

Why life for millions of smallholder farmers rests on a new eu directive, how is agritech helping to optimize the farming sector, the power of partnerships.

Machine learning, AI and precision fermentation are some of the many technology solutions that offer pathways for addressing the longest standing issues in our agri-food system. However, as standalone solutions and without commercially sustainable ecosystems, these technologies will not scale. To gain meaningful traction on these, innovations are also needed in the way we work together.

For this purpose, the World Economic Forum has launched the Food Innovation Hubs , along with several public, private and civil society stakeholders, to drive relevant high-end and low-cost inclusive innovation that could have a scalable impact. Taking a bottom-up and localized approach, these Food Innovation Hubs are under development in India, Colombia, Viet Nam, Europe, UAE and, in the scoping phase, in Kenya and Rwanda. Each hub has a different story and impact vision, including aspirations spanning from virtual to physical hubs.

In India, the Food Innovation Hub operating in the state of Madhya Pradesh has tapped Boomitra in leading a public-private partnership to scale its technologies towards a more sustainable approach to soil health that also improves the lives of farmers. The newest Food Innovation Hub in the UAE is exploring technology solutions to boost production in arid-climate ecosystems, while the Hub in Colombia is focusing on supporting farmers with year-round services and technology to deploy regenerative agriculture at scale.

To enable continued advancement, the Food Innovators Network brings these kinds of innovations within the hubs together, along with the best of the food systems innovation community to promote knowledge exchange, showcase the potential of investible technologies and facilitate opportunities to adopt them at scale. The Network has been engaging 200+ members and embarking on a global movement to unlock the power of public-private partnerships for the delivery and adoption of fit-for-purpose solutions.

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World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

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A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges

Doi:  10.1109/jas.2020.1003536.

• Xing Yang 1 ,  , 
• Lei Shu 2, 5 ,  ,  , 
• Jianing Chen 3 ,  , 
• Mohamed Amine Ferrag 4 ,  , 
• Jun Wu 3 ,  , 
• Edmond Nurellari 5 ,  , 
• Kai Huang 1 , 

College of Engineering, Nanjing Agricultural University, Nanjing 210031, China

School of Cyber Security, Shanghai Jiao Tong University, Shanghai 200240, China

Department of Computer Science, Guelma University, B.P. 401, 24000, Algeria

School of Engineering, University of Lincoln, Lincoln LN67TS, UK

the National Natural Science Foundation of China   62072248

the National Natural Science Foundation of China   61902188

in part by China Postdoctoral Science Foundation   2019M651713

Xing Yang (M'20) received the M.S. degree in control engineering from Nanjing University of Information Science and Technology, China, in 2018. He is currently working toward the Ph.D. degree in the college of engineering, Nanjing Agricultural University, China. His current research interests include fault diagnosis in wireless sensor networks, agricultural Internet of Things reliability, and machine learning algorithm

Lei Shu (SM'07) received the B.S. degree in computer science from South Central University for Nationalities, China, in 2002, and the M.S. degree in computer engineering from Kyung Hee University, South Korea, in 2005, and the Ph.D. degree from the Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland, in 2010. Until 2012, he was a Specially Assigned Researcher with the Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University, Japan. He is currently a Distinguished Professor with Nanjing Agricultural University, China, and a Lincoln Professor with the University of Lincoln, U.K. He is also the Director of the NAU-Lincoln Joint Research Center of Intelligent Engineering. He has published over 400 papers in related conferences, journals, and books in the areas of sensor networks and Internet of Things. His current H-index is 58 and i10-index is 218 in Google Scholar Citation. His current research interests include wireless sensor networks and Internet of Things. He has also served as a TPC member for more than 150 conferences, such as ICDCS, DCOSS, MASS, ICC, GLOBECOM, ICCCN, WCNC, and ISCC. He was a recipient of the 2014 Top Level Talents in Sailing Plan of Guangdong Province, China, the 2015 Outstanding Young Professor of Guangdong Province, and the GLOBECOM 2010, ICC 2013, ComManTel 2014, WICON 2016, SigTelCom 2017 Best Paper Awards, the 2017 and 2018 IEEE Systems Journal Best Paper Awards, the 2017 Journal of Network and Computer Applications Best Research Paper Award, and the Outstanding Associate Editor Award of 2017, and the 2018 IEEE ACCESS. He has also served over 50 various Co-Chair for international conferences/workshops, such as IWCMC, ICC, ISCC, ICNC, Chinacom, especially the Symposium Co-Chair for IWCMC 2012, ICC 2012, the General Co-Chair for Chinacom 2014, Qshine 2015, Collaboratecom 2017, DependSys 2018, and SCI 2019, the TPC Chair for InisCom 2015, NCCA 2015, WICON2016, NCCA2016, Chinacom2017, InisCom2017, WMNC 2017, and NCCA 2018

Jianing Chen received the B.S. degree (with Hons.) from the School of Electronic and Information Engineering in Beihang University, Beijing, China, in 2018. He is currently working toward the M.S. degree at the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China. His current research interests include industrial control systems (ICS), blockchain and cryptography

Mohamed Amine Ferrag received the Bachelor's degree (June, 2008), Master's degree (June, 2010), Ph.D. degree (June, 2014), HDR degree (April, 2019) from Badji Mokhtar-Annaba University, Algeria, all in Computer Science. Since October 2014, he is a senior lecturer at the Department of Computer Science, Guelma University, Algeria. Since July 2019, he is a Visiting Senior Researcher, NAU-Lincoln Joint Research Center of Intelligent Engineering, Nanjing Agricultural University, China. His research interests include wireless network security, network coding security, and applied cryptography. He has been conducting several research projects with international collaborations on these topics. He has published more than 60 papers in international journals and conferences in the above areas. Some of his research findings are published in top-cited journals, such as the IEEE Communications Surveys & Tutorials, IEEE Internet of Things Journal, IEEE Transactions on Engineering Management, IEEE Access, Journal of Information Security and Applications (Elsevier), Transactions on Emerging Telecommunications Technologies (Wiley), Telecommunication Systems (Springer), International Journal of Communication Systems (Wiley), Sustainable Cities and Society (Elsevier), Security and Communication Networks (Wiley), and Journal of Network and Computer Applications (Elsevier). He has participated in many international conferences worldwide, and has been granted short-term research visitor internships to many renown universities including, De Montfort University, UK and Istanbul Technical University, Turkey. He is currently serving on various editorial positions such as Editorial Board Member in Journals (Indexed SCI & Scopus) such as, IET Networks and International Journal of Internet Technology and Secured Transactions (Inderscience Publishers). He has served as an Organizing Committee Member (Track Chair, Co-Chair, Publicity Chair, Proceedings Editor, Web Chair) in numerous international conferences such as ICNAS'13'15'17, ASD'16, EUSPN'17, (AINIS) Symposium'17, ANT'17, SEIT'17, IEEE ICCE'18'19, and IEEE ITIA'20

Jun Wu (M'08) received the Ph.D. degree in information and telecommunication studies from Waseda University, Japan. He was a Post-Doctoral Researcher with the Research Institute for Secure Systems, National Institute of Advanced Industrial Science and Technology, Japan, from 2011 to 2012. He was a Researcher with the Global Information and Telecommunication Institute, Waseda University, from 2011 to 2013. He is currently an Associate Professor of Electronic Information and Electrical Engineering with Shanghai Jiao Tong University, China. His research interests include the advanced computation and communications techniques of smart sensors, wireless communication systems, industrial control systems, wireless sensor networks, and smart grids. He has hosted and participated in several research projects for the National Natural Science Foundation of China, National 863 Plan, and 973 Plan projects. He has been a Guest Editor of the IEEE SENSORS journal and a TPC Member of several international conferences, including WINCON 2011 and GLOBECOM 2015

Edmond Nurellari received his B.Sc and his M.Sc degree in Electrical and Electronic Engineering, both from Eastern Mediterranean University, Northern Cyprus, in 2010 and in 2012 respectively. From September 2010 to February 2013, he served as a Research and Teaching Assistant in the department of Electrical and Electronic Engineering at Eastern Mediterranean University. In 2013, he was awarded the Leeds International Research Scholarship (LIRS) to pursue his Ph. D. at the School of Electronics and Electrical Engineering, University of Leeds, United Kingdom. Since April 2017, he has been a faculty member with the School of Engineering at the University of Lincoln, United Kingdom, where he is currently a Lecturer in Electrical Engineering/Robotics. His research interests includes distributed signal processing, signal processing on graphs, resource allocations, distributed decisions and network security analysis in wireless sensor networks by employing tools from graph theory and game theory. He has served as an Invited Reviewer for the IEEE Transactions on Signal and Information Processing over Networks, IEEE Communication Letter, Springers Wireless Networks Journal, Springers Digital Signal Processing Journal and IEEE Flagship conferences

Kai Huang received the Ph.D. degree in agricultural engineering from China Agricultural University, China, in 2018. He is currently a Postdoctoral Researcher in the Department of Electrical Engineering at Nanjing Agricultural University, Nanjing, China. His main research includes agricultural Internet of Things. He has guest edited the special issue on Smart Agricultural Applications with Internet of Things in Sensors. He has served as a PC Members in the IoT Computing Systems area of 3PGCIC-2019

• Corresponding author: Lei Shu.e-mail: [email protected]
• Recommended by Associate Editor MengChu Zhou.
• Revised Date: 2020-10-01
• Accepted Date: 2020-11-17
• Agricultural artificial intelligence , 
• agricultural automation , 
• agricultural Internet of Things , 
• security , 
• smart agriculture

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通讯作者: 陈斌, [email protected].

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• Figure 1. Characteristics and confronted issues of agriculture development (from Agriculture 1.0 to Agriculture 4.0).
• Figure 2. Organization of this paper. Development modes, 7 key technologies, and 11 applications of smart agriculture are summarized in Section Ⅱ and Ⅲ, which contribute to discuss security issues in Section Ⅳ, Ⅴ and Ⅵ.
• Figure 3. Research fields of smart agriculture based on 1) precision agriculture, 2) facility agriculture, and 3) order agriculture.
• Figure 4. Various technologies and applications based on IoT architecture.
• Figure 5. Indoor conditions and outdoor environment of poultry and livestock breeding, e.g., (a) indoor conditions for chicken farm, (b) outdoor environment for sheep breeding, (c) indoor conditions for hog farm, and (d) outdoor environment for cattle breeding.
• Figure 6. SIL-IoT component and communication diagram. The insecticidal range is smaller than the communication range, which ensures that the SIL-IoT nodes deployed in the field meet the communication requirements.
• Figure 7. Massive data fusion among satellites, aircraft, UAVs, and surface stations.
• Figure 8. Comparison of edge computing and cloud computing. The advantages of edge computing are low cost and low delay, which benefit to high time efficiency tasks, e.g., SIL-IoT, agricultural UVAs, driverless tractors.
• Figure 9. (a) Fixed-wing UAV, (b) single-rotor hydraulic UAV, and (c) multi-rotor electric UAV. Currently, electric multi-rotor UAVs have a major market share due to their low cost, low failure rate, simple operation, and environmental protection.
• Figure 10. Different security and privacy countermeasures for 7 key technologies and 11 key applications in smart agriculture.
• Figure 11. Interference on different distances between SIL and receiving node. Average communication duration values of 0 cm, 5 cm, and 10 cm are 45.6 s, 116.5 s, and 765.9 s. Maximum values of 0 cm, 5 cm, and 10 cm are 151 s, 422 s, and 1970 s. Minimum values of 0 cm, 5 cm, and 10 cm are 4 s, 4 s, and 45 s.
• Figure 12. Data acquisition under interference. For the first line, "2020-07-08-15-15:34:32" is time format, "02" is sensor ID in red dotted box, "26.6" is temperature (℃) in yellow dotted box, and "72" is relative humidity (%) in green dotted box. Besides, data in blue dotted box are normal data, data in violet dotted box are abnormal data, and data in grey dotted box are device reset information.
• Figure 13. When a variety of data is transmitted to the cloud or edge node, hackers access the database through unauthorized access.
• Figure 14. In the process of node communication, hackers intercept data by invading receiving nodes.
• Figure 15. System fault, link fault, sensor fault, and other factors lead to uneven spraying of pesticides.
• Figure 16. Due to malicious data attacks, the solar insecticidal lamps in an actual pest outbreak area are sleeping, which leads to crops damage. Simultaneously, we have a waste of energy caused by incorrect responses of the solar insecticidal lamps that are in the false pest outbreak area.
• Figure 17. A driverless tractor operating on the wrong route after a control system intrusion.
• Figure 18. The novel mode of agricultural production by FoE architecture.
• Figure 19. The security scheme of renewable EMS, including 1) fault self-diagnosis of hardware and software in agricultural facilities and renewable energy equipment, 2) identity authentication and data encryption in data transmission layer, 3) intrusion detection and data backup in background management.
• Figure 20. A plant phenotype information system based on SDN architecture (including multiple data sources, control pane, data pane, and application layer) and security issues (trust between applications and controller, wrong control instructions, and abnormal data flow) result from hacker attacks.
• Figure 21. Future research trends, security challenges, and lessons learned about smart agriculture.

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Agriculture Technology

Modern farms and agricultural operations work far differently than those a few decades ago, primarily because of advancements in technology, including sensors, devices, machines, and information technology. Today’s agriculture routinely uses sophisticated technologies such as robots, temperature and moisture sensors, aerial images, and GPS technology . These advanced devices and precision agriculture  and robotic systems  allow businesses to be more profitable, efficient, safer, and more environmentally friendly.

Importance of Agricultural Technology

Farmers no longer have to apply water, fertilizers, and pesticides uniformly across entire fields. Instead, they can use the minimum quantities required and target very specific areas, or even treat individual plants differently. Benefits include:

• Higher crop productivity
• Decreased  use of water, fertilizer, and pesticides, which in turn keeps food prices down
• Reduced impact on natural ecosystems
• Less runoff of chemicals into rivers and groundwater
• Increased worker safety

In addition, robotic technologies enable more reliable monitoring and management of natural resources, such as air and water quality. It also gives producers greater control over plant and animal production, processing, distribution, and storage, which results in:

• Greater efficiencies and lower prices
• Safer growing conditions and safer foods
• Reduced environmental and ecological impact

NIFA’s Impact

NIFA advances agricultural technology and ensures that the nation’s agricultural industries are able to utilize it by supporting:

• Basic research and development in physical sciences, engineering, and computer sciences
• Development of agricultural devices, sensors, and systems
• Applied research that assesses how to employ technologies economically and with minimal disruption to existing practices
• Assistance and instruction to farmers on how to use new technologies

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Online Trade Magazine Agricultural Technology and Precision Farming

How Modern Agriculture Technology is Changing in 2022

Once a labour-intensive industry is now a smart, data-driven one. Modern agriculture technologies such as crop monitoring drones, farm management software and livestock measuring sensors are just some of the tools improving crop production in a world full of depleting resources.

Never before have these technologies been more important given the world’s population is expected to hit 9.7 billion by 2050, with the potential to balloon to nearly 11 billion by the turn of the century. It is estimated that future generations will therefore need to produce at a minimum double the quantity of food we do today, all while balancing the finite resources available.

This is where modern agriculture technology steps in. With as much as $22.3 billion being invested into the space in 2020, the opportunities new tools and software can offer farmers will be essential to the future of the industry.

With this in mind, the AgTech Recruitment experts, Storm4, predict how modern agriculture technology is improving the day-to-day struggles of farmers, taking the industry to the next level of innovation in 2022.

The Advancements Of Modern Agriculture Technology

Connected Technologies Are Progressively Preserving Resources

Keeping track of the quality and lifetime of resources is increasingly becoming difficult, particularly as farms are dotted throughout a large network of locations. Thanks to connected technologies, farmers can obtain real-time information on the state of their resources and goods, avoiding careless waste.

GrainSafe, developed by Telesense, is an excellent example of this. The California-based start-up is a leader in the post-harvest data revolution, using an advanced suite of sensors to provide grain managers with actionable insights about their stored grain. Real-time information on the temperature and humidity of a mound of grain allows farmers to alter circumstances, ensuring their grain is stored in the best possible condition at all times.  

Machine Learning Algorithms Promoting Improved Soil Health

Pets and diseases have been an ongoing battle for farmers since the dawn of human civilization, resulting in underperforming harvests. In many cases, the source of these fatal yields is frequently detected in the soil crops are cultivated.

Despite being renowned for its work in medicine, the assistance of machine learning algorithms is providing the very solution farmers have been waiting for. A great example of this is California-based start-up, Pattern Ag. Its machine learning discovery platform is centred around a deep understanding of soil, being able to detect symptoms of diseases and pests and propose what herbicides to use to eliminate the issue.  

Machine learning algorithms have also been effective in assisting farmers to hit peak efficiency, regarding the application of chemicals to ward off pests and diseases. An example is DTN’s wireless ‘smart trap’, equipped with pheromones that allow the farmer to see the insect population in real-time via its app. By automating the error-prone process of determining the amount of pesticide required, the company promises to save $20 billion in crop insect damage in the United States.

The Arrival Of Autonomous Robots

Automation in farming is a key driver in making farms more efficient, meaning prices are reduced for customers, the environmental footprint of farms is lower and labour costs are slashed. From autonomous tractors and robotic harvesters to automatic watering and seeding robots, an increasing number of traditional agriculture companies are starting to adopt farm automation into their processes.

An example of this is American Robotics. Using innovations in AI and robotics, it has developed a fully autonomous Robot-as-a-Service (RaaS) tool that continuously monitors, digitize and analyse assets in real-time. The automated robotic data platform and drone currently stands as the only drone system in the AgTech industry capable of continuous, unattended operation.

Rowbot Systems is also using robots to collect valuable data to inform current and future work. Their first, commercialised robot is a compact, self-driving, multi-purpose platform that moves between rows of corn, eliminating any obstacles of height restraints imposed by the rapid growth of crops. The robots work collectively in groups to apply nitrogen fertiliser in time with corn needs, and seed cover crops into tall corn. 

Modern agriculture technology has established a firm grip on agricultural development over the last decade. Without this revolutionary approach to farming, efficiency in farms will deplete whilst crops will continue to be produced with devasting impacts on the environment, destroying the scarce land that is necessary to continue to grow crops for years to come.

Storm4 are leaders in global GreenTech recruitment, connecting organisations with senior talent to drive their mission of a sustainable future. With institutional backing from Puffin Point Investments, the company has over $3m in Series A funding to disrupt the GreenTech recruitment market. Their highly specialised teams cover key GreenTech skillsets across C-Suite in Product Management, Engineering, Sales, Marketing and Data & Analytics. They are a leading provider of GreenTech-focused information to clients for market compensation and best practice in Diversity, Equity & Inclusion, hiring and retention.

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What Is Modern Technology In Agriculture

Modern technology in agriculture is a broad term encompassing a wide range of methods and applications that have been developed to increase agricultural productivity. It includes the use of modern agricultural tools and equipment, as well as using sophisticated technologies such as drones and GPS tracking. By leveraging these technologies, farmers are able to reduce labor costs, conserve resources, and maximize yield. The benefits of integrating modern technology with traditional agricultural practices are numerous, and the advancements made in recent years have made it possible for farmers to increase their productivity and profitability.

Precision Agriculture

One of the most popular technology-based approaches used in modern agriculture is known as precision agriculture. This approach is used to maximize production and minimize costs through the use of GPS-guided technology, data-driven decisions, and automated processes. Precision agriculture relies on the collected data to accurately administer the right amount of fertilizer, water, and pesticide across entire fields or small plots of land. This has led to more efficient farming practices and a minimization of inputs, resulting in cost savings and improved yields.

Remote Sensing

Remote sensing is another technology found in modern agricultural practices. These devices measure certain physical qualities such as light, temperature, moisture levels, and soil fertility. This allows farmers to more accurately identify areas that need attention without having to physically visit them. Remote sensing can also be used to monitor crop performance, map out crop field patterns, and provide real-time data to producers.

Drones are quickly becoming a popular tool for farmers. These unmanned aerial vehicles (UAVs) are equipped with cameras and other sensors that can capture images and data from above. This data can be used to monitor crop health, identify diseased areas, and even optimize irrigation systems. Drones have also proven to be useful for crop spraying, reducing the need for manual labor.

Automation is another major component of modern technology in agriculture. Automation technologies allow farmers to automate certain processes, such as harvesting and planting. Automation can help reduce labor costs, and in some cases, it can even improve yields. Automation can also be used to monitor soil and water conditions in a field and to notify farmers when it’s time to irrigate.

Robotics is another application of modern technology in agriculture. Robots can be used for harvesting, weeding, and other labor-intensive tasks that have traditionally been done by hand. This allows for higher precision and accuracy compared to manual labor. Robotics technology can also be used to monitor crop health, detect pests, and increase efficiency in many other areas of the agricultural industry.

Data Science

Data science is an emerging technology in the agricultural industry. Data science is the practice of analyzing large sets of data to extract meaningful insights that can be used to improve agricultural processes. Through data science, farmers can identify trends, optimize yields, and better predict crop performance. Data science can also be used to develop better practices for farming, pest control, and crop rotation.

Artificial Intelligence

Artificial intelligence (AI) is another important technology found in modern agriculture. AI can be used to analyze data, monitor crop health, and automate irrigation systems. It can also be used to identify disease in crops, detect pests, and predict yields. As AI technology advances, it is expected to be used increasingly in the agricultural industry to help increase the efficiency and yield of crops.

Greenhouse and Controlled Environment Agriculture

The use of greenhouses and controlled environment agriculture (CEA) is another technological advancement found in modern agriculture. Greenhouses and CEA allow farmers to control temperature, humidity, light, and other variables in order to maximize crop yields. They also reduce the need for chemicals and other inputs, resulting in a more sustainable and efficient farming process.

Hardware Components

Hardware components, such as sensors and monitoring devices, are essential to the advancement of modern technology in agriculture. These components allow farmers to collect data and monitor crop health remotely. In addition, these components can be used to automate irrigation, identify disease and pests, and control temperature and humidity in greenhouses and CEA.

Gene Editing

Gene editing is another key technology being used in modern agriculture. This technology allows for the insertion or deletion of DNA sequences in order to alter the genetic makeup of plants. This can be used to make them more resistant to disease, drought, and pests. Gene editing has become increasingly popular due to its potential to significantly improve crop yields.

Weather Prediction and Modelling

Weather prediction and modelling are two essential technologies in modern agriculture. The ability to predict weather patterns allows farmers to plan and make decisions that are appropriate for the current conditions. Weather modelling provides farmers with a better understanding of how weather affects crop yields. As weather conditions can often be unpredictable, the ability to accurately predict and model weather conditions can provide farmers with invaluable insight.

Eduardo Villanueva

Eduardo Villanueva is an expert on agricultural sciences, with decades of experience in the field. With a passion for teaching others, Eduardo has written extensively about topics related to sustainable agriculture and food security. His work aims to empower rural farmers and promote responsible farming practices that help preserve the environment for future generations. A dedicated family man, Eduardo lives in central Mexico with his wife and children. He is always looking for ways to connect people and knowledge to create positive changes in their local communities.

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The Extent of Effectiveness of Modern Agricultural Technology Among Farmers in Barangay, Butong, City of Cabuyao, Laguna

2022, The Extent of Effectiveness of Modern Agricultural Technology Among Farmers in Barangay, Butong, City of Cabuyao, Laguna

This study relates to the extent and effectiveness of modern agricultural technology for farming. Throughout history, technology has been shown to be quite beneficial in agricultural industries. Farmers may now farm crops in previously thought-to-be-impossible regions, but this is only possible because of agricultural biotechnology. As a result, farmers can produce a large number of crops in a relatively short period of time. However, there has been a limit to how quickly modern agricultural technology is adopted by farmers. Some economic factors that can influence the adoption rate of agricultural technology are the size of the land, the cost, and the benefits from technology. Also, farmers' educational level, age, social group, and gender can all influence their likelihood of adopting new agricultural technology.

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Use of Modern Technology in Agriculture Sector

The most essential issue in the world today is the food supply. The demand for food has increased by more than twice the population growth rate over the past 40 years. In fact, according to a report by the Food and Agriculture Organization (FAO), about 11% of the global population, or 815 million people, are undernourished and do not have enough food for their active and healthy lives.

The article talks of how modern technology has been very helpful in the agriculture sector and it is helping to produce more and better quality of food, which is, in turn, helping to sustain life.

Let’s begin!

The Use of Modern Technology in the Agriculture Sector

The use of modern technology in the agriculture sector is vast. It has helped farmers in various ways. The adoption of new and improved technologies has increased the production and productivity of crops. It has also helped in reducing the cost of production. The use of technology has also made the farming process easier and more efficient.

Some of the popular technologies used in the agriculture sector are:

Top 8 Modern Technology In Agriculture Industry

1. Drones: Drones are being extensively used for mapping, surveying, and crop monitoring. They help in collecting data that can be used for the planning and execution of farming activities.

2. GPS Technology: GPS technology is widely used in precision farming. It helps in locating the field boundaries and applying fertilizers, pesticides, and herbicides accurately. This reduces wastage and increases efficiency.

3. Satellite Imagery: Satellite imagery is used for weather forecasting, crop monitoring, and yield analysis. It helps farmers take timely decisions regarding irrigation, cropping patterns, etc.

4. Automation: Automation solution has been widely adopted in agricultural processes like sowing, transplanting, harvesting, etc. This has reduced the dependence on manual labor and increased efficiency.

5 . IoT Sensors: IoT sensors are being used for soil moisture monitoring, weather tracking, etc. This information helps farmers to make better decisions about irrigation, inputs application, etc.

6. Soil sensors: Soil sensors are used to measure soil moisture levels, temperature and other factors that affect crop growth. The data collected by the sensors is transmitted wirelessly to the farmer, who can then adjust his or her farming practices accordingly.

7. Weather monitoring: Farmers can now access real-time weather data that can help them make decisions about when to plant, how to irrigate, and what type of crop to grow. This information can be accessed via weather apps or websites, or through dedicated weather stations on the farm.

8. Agricultural robots: Agricultural robots are being developed to carry out various tasks on farms, such as milking cows, picking fruits and vegetables, and even mowing grass. These robots can work long hours without tiring, and can often do a better job than human workers.

9. Artificial Intelligence:  Artificial intelligence (AI) has revolutionized the field of agriculture. Contemporary AI-based technology and tools facilitate the collection of data and aid in the practice of precision farming and making well-informed decisions. Drones, remote sensors, and satellites continuously collect data on weather patterns in and around the fields, offering farmers crucial information on temperature, rainfall, soil moisture, and other relevant factors.

Nevertheless, artificial intelligence faces limited adoption in a country such as India due to obstacles such as small-scale farming, divided land ownership, and other factors. However, it is undeniable that AI-based technologies have the potential to enhance accuracy in large-scale agriculture and result in a significant increase in output.

Do you know? How AI is Transforming the Agriculture Industry

Importance of Modern Technology In Agriculture Industry

Modern technology in agriculture has led to increased production and productivity. This has in turn led to improved food security and incomes for farmers. In addition, it has helped to create new jobs and improve the quality of life for rural communities.

Vertical farms use up to  70% less water  than traditional farms.

Technology Boosts Agricultural Productivity

Technology has played a key role in increasing agricultural productivity. For example, the use of mechanization has reduced the need for manual labor, thus increasing efficiency and output. The introduction of irrigation systems has also helped to boost production by making it possible to grow crops in otherwise arid areas. In addition, modern technology has made it possible to develop high-yielding crop varieties that are resistant to pests and diseases.

The use of technology in agriculture has also had a positive impact on food security. Increasing production has helped to ensure that more people have access to nutritious and affordable food.

Agricultural Technology can Create New Jobs

Technology can also help to create new jobs in the agricultural sector. For instance, the use of mobile phones and other digital technologies is providing new opportunities for farmers to connect with markets and sell their products directly to consumers. In addition, the development of value-added services such as agro-tourism is creating new employment opportunities in rural areas.

Checkout Our Trending Blog: How Technology Is Boosting the Agriculture Industry

Impact of Agricultural Technology on Farmers

In recent years, there has been a significant impact of agricultural technology on farmers across the globe. With the help of technology, farmers are now able to increase their yields and produce more crops than ever before. In addition, they can also reduce their costs by using less labor and inputs.

However, there are also some drawbacks to using technology in agriculture. One of the main problems is that it can lead to over-dependence on machines and chemicals, which can be expensive to maintain. Moreover, if not used properly, it can also damage the environment.

Impact of Agricultural Technology on Consumers

Agricultural technology has impacted consumers in many ways. The use of modern technology has helped farmers to increase the production of crops and livestock. It has also helped to improve the quality of the products. The use of new technology has also reduced the cost of production.

The adoption of new technology has also led to the development of new methods of marketing and distribution of agricultural products. This has helped the farmers to reach a wider market for their products. The use of technology has also helped to create new jobs in the agricultural sector.

Also Read: Agriculture Website Designing To Increase Your Agri-Business

Impact of Modern Technology on Agriculture

Modern technology has had a significant impact on agriculture, revolutionizing the industry and improving productivity, efficiency, and sustainability. Here are some key ways in which technology has influenced agriculture:

• Precision farming: Technologies such as GPS, remote sensing, and Geographic Information Systems (GIS) enable farmers to gather precise data about their fields. This data helps them optimize the use of fertilizers, water, and pesticides, leading to improved crop yields and reduced environmental impact.
• Automation: Agricultural machinery has become increasingly using automation solutions , reducing the need for manual labor and improving efficiency. Tractors equipped with GPS systems can navigate fields accurately, and automated machinery can perform tasks like planting, harvesting, and sorting crops with precision and speed.
• Remote monitoring: Internet of Things (IoT) devices and sensors allow farmers to remotely monitor various aspects of their operations. They can track soil moisture levels, temperature, humidity, and crop growth patterns, helping them make informed decisions about irrigation, pest control, and crop management.
• Genetic engineering and biotechnology: Advances in biotechnology have led to the development of genetically modified (GM) crops with improved traits such as disease resistance, drought tolerance, and higher nutritional value. These crops offer increased yields and reduced pesticide usage.
• Data analytics Solutions: The collection and analysis of large amounts of data from farm operations have become crucial in making informed decisions. Farmers can analyze data on weather patterns, crop performance, soil conditions, and market trends to optimize their farming practices, minimize risks, and maximize profits.
• Vertical farming and indoor agriculture: With the help of technology, indoor farming systems, such as hydroponics, aquaponics, and vertical farming, have gained popularity. These methods allow year-round cultivation in controlled environments, conserve water and land, and reduce transportation costs.
• Drones and satellite imagery: Drones equipped with high-resolution cameras and sensors can capture detailed images of fields. These images help identify crop health, detect pest and disease outbreaks, and assess irrigation needs. Satellite imagery provides a broader perspective, allowing farmers to monitor large areas and make data-driven decisions.
• Farm management software : Specialized software applications assist farmers in managing their operations efficiently. These tools enable them to plan and track activities, manage inventories, analyze financials, and streamline logistics, improving overall farm management.

Overall, modern technology has transformed agriculture, making it more productive, sustainable, and resilient. By leveraging these advancements, farmers can optimize their resources, reduce environmental impact, and meet the growing demand for food in a rapidly changing world.

Success Storie­s on Tech Use in Agriculture

1. pre­cision farming in the us: .

In the­ U.S., precision farming uses things like GPS mapping, soil scanning, data manage­ment, and IoT tech to get be­tter crops while using fewe­r resources. Farmers fly drone­s over crops, use satellite­ images to check soil health, and use­ self-driving tractors for planting and harvest. This makes farming more­ efficient and sustainable.

Impact: Farms that use­ these technique­s have seen the­ir crops grow about 20% more on average. Some­ farms have used up to 70% less he­rbicide. This approach doesn’t just help grow he­althier crops and more of them, it also me­ans farming is not as harmful to the environment.

2. Singapore: A Hub for Te­ch-Driven Vertical Farms

Singapore is utilizing high-te­ch vertical farms that merge automation, artificial inte­lligence, and aeroponics to grow ve­getables. Because­ these farms use le­ss water and land and avoid chemical pesticide­s, they’re a gree­ner substitute for old-fashioned farming.

Impact: Vertical farming in Singapore? Up to 10 times highe­r yields per square me­ter than typical farms. This advance is key for countrie­s where there­’s not much land to grow crops. It’s a shining example of urban farming that can be copie­d all over the globe.

Final Words

Agricultural productivity has increased manifold with the use of modern technology. In fact, it is one of the few sectors where labor-saving devices have been fully utilized. Today, a farmer can do the work of several men and women with the help of machines. This not only saves time but also reduces cost and increases output.

If you have any concerns about agricultural technology, you can contact  Aeologic Technologies!

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