case study on agriculture ppt

Organic Farming – Case studies

Dec 16, 2013

1.16k likes | 6.03k Views

Organic Farming – Case studies . Done by: David Joel Loo 3i202 Yong Han Shawn 3i223. What is Organic Farming?. Organic farming is a farming method that aims to work in harmony with nature rather than against it

Share Presentation

• chemical fertilizer farming system
• www organicfoodinfo
• conventional cropping systems
• soil health
• yong han shawn

Presentation Transcript

Organic Farming – Case studies Done by: David Joel Loo 3i202 Yong Han Shawn 3i223

What is Organic Farming? • Organic farming is a farming method that aims to work in harmony with nature rather than against it • This involves using techniques to achieve good crop yields without harming the environment. • It includes keeping and building a good soil structure and soil fertility, as well as controlling pests, diseases and weeds.

Organic Farming • Organic farming relies on techniques such as crop rotation, green manure, compost, biological pest control, and mechanical cultivation to maintain soil productivity and control pests on a farm. • It excludes or strictly limits the use of synthetic fertilizers and synthetic pesticides, plant growth regulators, livestock antibiotics, food additives, and genetically modified organisms.

Why use Organic Farming? • Organic Farming is able to provide long term benefits to the people and to the environment: • Increase long-term fertility of the soil • Control pest and diseases without harming the environment • Ensure that water stays clean and safe • Food produced are nutritious and are of a high quality which can be sold for a good price • It is a type of farming in which farmers can save money and the results are more beneficial as compared to other type of farming.

Why use Organic Farming? • It helps to increase the fertility of soil and also help in reducing soil erosion. • It consume less water as compared to other farming and give more productivity of crops • Without any kind of  chemical fertilizations it helps farmers to reduce the effect of weeds by using garlic, clove oil etc • It can produce new variety of crops on the same land on which other techniques give only single type. • It produces only pure crops without any toxic materials which are harmful for the human life and this increases the profit also.

Problems with Organic Farming • Dependency on fertilizers. Greater amount are needed every year to produce the same yields of crops • Pests and diseases may turn more difficult to control as they become resistant to artificial pesticides • Farming with organic methods gives smaller crops as compared to artificial farming. • Organic farming gives low productivity of vegetables such as potatoes in the same areas where the conventional farming produces more.

Problems with Organic Farming • It enhances the emission of carbon dioxide and this may produces bad effect in the climate. • As carbon dioxide is stored in these crops so it is harmful for the human life and can be dangerous for us. • It is also known that the food which is derived from organic resources is very harmful because it produces E.coli bacterial infection.

Organic grain and soybean production in the Midwestern United States • A comprehensive review of comparison studies of grain and soybean production conducted by Midwestern universities since 1978 found that in all these studies organic production was equivalent to, and in many cases better than, conventional (Welsh, 1999). • Organic systems had higher yields than conventional systems which featured continuous crop production (no rotations) and equal or lower yields in conventional systems that included crop rotations. • In the drier climates such as the Great Plains, organic systems had higher yields, as they tend to be better during droughts than conventional systems.

Organic grain and soybean production in the Midwestern United States • In one such study in South Dakota for the period 1986-1992, the average yields of soybeans were 29.6 bushels/acre and 28.6 bushels/acre in the organic and conventional systems respectively. • In the same study, average spring wheat yields were 41.5 bushels/acre and 39.5 bushels/acre in the organic and conventional systems respectively.

Organic grain and soybean production in the Midwestern United States • When comparing the profitability of farming systems, the study found that organic cropping systems were always more profitable than common conventional cropping systems. • This was attributed to lower production costs and the ability of organic systems to outperform conventional in drier areas, or during drier periods.

Broadbalk experiment in the UK • This experiment compares a manure based fertilizer farming system to a synthetic chemical fertilizer farming system. • Wheat yields are shown to be on average slightly higher in the organically fertilized plots (3.45 tones/hectare) than the plots receiving chemical fertilizers (3.40 tones/hectare).

Broadbalk experiment in the UK • Most importantly though, soil fertility, measured as soil organic matter and nitrogen levels, increased by 120% over 150 years in the organic plots, compared with only 20% increase in chemically fertilized plots (Jenkinson, 1994).

Comparison of conventional and organic farms in California. • A study which compared ecological characteristics and productivity of 20 commercial farms in the Central Valley of California gives us a better understanding of how a conversion to organic would fare in a commercial farm setting.

Comparison of conventional and organic farms in California. • Tomato yields were shown to be quite similar in organic and conventional farms (Drinkwater, 1995). Insect pest damage was also comparable in both cases of organic and conventional farms. • However, significant differences were found in soil health indicators such as nitrogen mineralization potential and microbial abundance and diversity which were higher in the organic farms. Nitrogen mineralization potential was three times greater in organic compared to conventional fields. The organic fields also had 28% more organic carbon. The increased soil health in the organic farms resulted in considerably lower disease incidence. Severity of the most prevalent disease in the study, tomato corky root disease, was found to be significantly lower in the organic farms (Drinkwater, 1995).

Sources • Shashank Nakate. "Organic Farming Methods." Intellegient Life on the Web. 2010. Buzzle.com. undefined. <http://www.buzzle.com/articles/organic-farming-methods.html>. • . "What is Organic Farming ?." We support Green. . . 29 August 2010. <http://www.solarpowernotes.com/environmental-sceince/what-is-organic-farming.html>. • Christos Vasilikiotis, Ph.D.. "Can Organic Farming "Feed the World"?." Organic Farming. . University of California, BerkeleyESPM-Division of Insect Biology. 17 August 2010. <http://www.cnr.berkeley.edu/~christos/articles/cv_organic_farming.html>. • Mrs Suzanne Stockwell . "Case StudiesOrganic Farming in Scotland." Geography for Schools. 2010 . SAC . 31st July 2010. http://www.sac.ac.uk/learning/geography/agriculture/organicfarming/orgacasestudies • Valerie Elliott, Countryside Editor. "Let us bend the rules, say organic farmers." The Times. 22 December 2008. . 31st July 2010. <http://www.timesonline.co.uk/tol/life_and_style/food_and_drink/article5380546.ece>. • Jon Ungoed-Thomas. "Official: organic really is better." The Times. 28 October 2007. . 31st July 2010. <http://www.timesonline.co.uk/tol/news/uk/health/article2753446.ece>. • Valerie Elliott, Countryside Editor. "Case study: Organic farming: 'It could take five years'." The Times. 22 December 2008. The Sunday Times. 31st July 2010. <http://www.timesonline.co.uk/tol/life_and_style/food_and_drink/article5380549.ece>.

Sources • Jane Thurnell. "What Are The Advantages Of Organic Farming." What Are The Advantages Of Organic Farming. . . 31st July 2010. <http://www.organicfoodinfo.net/What_Are_The_Advantages_of_Organic_Farming.php>. • . "Advantages and Disadvantages Organic Farming pro and cons ." Advantages and Disadvantages Organic Farming: Good Things, Barriers and Environmental Effects. . . 31st July 2010. <http://www.small-farm-permaculture-and-sustainable-living.com/advantages_and_disadvantages_organic_farming.html>. • HDRA. "What is Organic Farming." What is Organic Farming. . . 31st July 2010. <http://www.infonet-biovision.org/res/res/files/488.OrgFarm.pdf>. • undefined. "Organic farming." The Free Encyclopedia. undefined. Wikipedia, the free encyclopedia. 11th August 2010. <http://en.wikipedia.org/wiki/Organic_farming>. • undefined. "Organic farming methods." Science Reference. July 24, 2007. Science Daily. 11th August 2010. <http://www.sciencedaily.com/articles/o/organic_farming_methods.htm>.

Thank you for your attention Any Questions?

• More by User

Organic Farming

Organic Farming. Making the Transition Cissy Bowman, CEO, Indiana Certified Organic. Bringing Land into Organic Production. What is organic transition/conversion? What do you need to do to get certified organic? Is organic right for you? Why go organic?. “Transition” means:.

1.75k views • 37 slides

Organic Farming and the Social Economy

Organic Farming and the Social Economy. Jennifer Sumner and Sophie Llewelyn Community-University Research Alliance for Southern Ontario ’ s Social Economy University of Guelph ~

607 views • 11 slides

Organic Farming With A Disability

Organic Farming With A Disability. Andy and Hilda Byrd Whippoorwill Hollow Organic Farm. Overview. Whippoorwill Hollow Organic Farm. Overview of our farm. AgrAbility in GA. Our involvement with the AgrAbility in GA Program. Organic Farming.

597 views • 30 slides

Changes in bowel movements ( IBS )

Changes in bowel movements ( IBS ). Sattam alsulami Nasser AlQahtani Amer Almutairi. True or False. People suffering from irritable bowel syndrome for more than 10 years develop cancer. Truth False. True or False.

755 views • 49 slides

Qualitative Studies: Case Studies

Qualitative Studies: Case Studies. Introduction. In this presentation we will examine the use of case studies in testing research hypotheses: Validity; Quality; Analysis. Case study methodology.

701 views • 30 slides

Organic food

Organic food. Kadri Kalle, Sustainable SCANdinavia. What is organic food?. Organic food is a product of a farming system that uses natural and regenerative processes: crop rotation; animal and plant manuring; mechanical weeding; biological pest control.

852 views • 15 slides

Risk Based Inspections in organic farming

Improving the Organic Certification System Workshop in Brussels, October 14, 2011 . Risk Based Inspections in organic farming. Raffaele Zanoli Università Politecnica delle Marche, IT. A working definition of a RBI.

539 views • 19 slides

Commercial versus Subsistence Farming

Commercial versus Subsistence Farming. Done by: Chan Wei 06 Edmond Chen 07 Ng Zi Yao 25. Subsistence farming/agriculture. Commercial Farming/agriculture. Differences . Differences . Substinence commerical ?. Mindmap: Commercial farming. Case study: Slash and burn cultivation in Brazil.

715 views • 13 slides

Unit 1: World at risk case studies

Unit 1: World at risk case studies. Revision of disaster hotspot case studies Revision of climate change case studies. World at risk Core case studies. You will need to know these four core case studies You will be expected to know other examples and case studies to support your answers

780 views • 22 slides

Organic Farming Research in the Pacific Northwest • Challenges • Opportunities • Outlook

Organic Farming Research in the Pacific Northwest • Challenges • Opportunities • Outlook. D. Granatstein, A. Stone, C. Williams, C. Miles, D. Bezdicek, C. Perillo Washington State University, Oregon State University, University of Idaho. Organic pears near Chelan, WA.

447 views • 29 slides

Organic farming

Organic farming. Keith Vella 4.4 St.Ignatius College, Handaq. Definition of organic farming. Organic farming is a method farming where agricultural products grow in an environmentally friendly.

4.27k views • 8 slides

Preventing Another Dust Bowl

Preventing Another Dust Bowl. By: Emily Flynn and Chyna Law. Organic Farming Policy. All local farms must use organic agriculture methods. All local farms must also be certified organic by the state in order to continue production. Organic Farming :

136 views • 4 slides

PPP in Brazil Case Studies

Marcelo de Lima e Souza PPP Unit – Economic Advisory Ministry of Planning, Budget and Management Mexico, February 24 th 2010. PPP in Brazil Case Studies. PPP Case Studies. Case studies Datacenter Banco do Brasil Pontal Irrigation Project National Public Television Network

580 views • 20 slides

MANGO FARMING EXPERIENCE

MANGO FARMING EXPERIENCE. LEARNING FROM EXPERIENCE THE LOGIC AND REASONS OF NATURAL ORGANIC AND BIOLOGICAL FARMING FOCUS ON ORGANIC MANGO. TREND of FARMING. Before 1950, Natural farming practices 1960 – 2000, Industrial Farming 2000 – 2007, Organic natural farming

1.01k views • 28 slides

Welcome to everybody Fabio M. Santucci

Welcome to everybody Fabio M. Santucci. Organic Farming: European Market and Opportunities for Syria. # Holistic approach # Crisis of conventional farming # Definitions of Organic Farming # Development of OF # European situation # Marketing systems # Consumers’ profile # Prices

355 views • 18 slides

WP3 Case studies

WP3 Case studies. Why Case studies?. Case studies can: illustrate regional differences and sustainability conditions – sustainability profiles, (b) demonstrate the issues and above all mechanisms of impacts at relevant spatial scale(s),

361 views • 13 slides

by Jonathon Landeck, Deputy Executive Director

Organic Farming Research Foundation How OFRF and SARE Can Nurture and Grow Organic Agriculture Together. by Jonathon Landeck, Deputy Executive Director. OFRF Mission: Foster the widespread improvement and adoption of organic farming systems )

213 views • 8 slides

Can organic farming save the world?

Can organic farming save the world?. Royal Society-Wolfson Professor of Soils &amp; Global Change Institute of Biological &amp; Environmental Sciences, School of Biological Sciences, University of Aberdeen, Scotland, UK E-mail: [email protected]. Pete Smith.

379 views • 22 slides

SAP - ERP CASE STUDIES

SAP - ERP CASE STUDIES. www.sapendusers.com. by. SAP case studies. Means. Successful stories of SAP customers. SAP customers. Means. Companies in which SAP – ERP is successfully implemented. SAP case studies. We can find out answers in SAP case studies like.

1.07k views • 11 slides

Retrospective &amp; Prospective Studies Case Studies

Retrospective &amp; Prospective Studies Case Studies. ICRI Delhi. Hierarchy of Evidence. Anecdotal case reports Case series without controls Series with literature control Analyses using computer databases Case control studies Cohort studies Randomized control trials (RCTs)

1.01k views • 45 slides

Organic Farming: The Reality

Organic Farming: The Reality. By Josh Sonnabend. Background on Organic Farming. Organic Farming: USDA Consumer Brochure-Food that is produced without using conventional pesticides, fertilizers, or bioengineering $ 2.6 billion to $26.7 billion increase in sales since 1997

367 views • 8 slides

Bram Moeskops Scientific Coordinator

New Strategic Research and Innovation Agenda for Organic Food and Farming. CORE Organic Research seminar 1 October 2014, Stockholm. Bram Moeskops Scientific Coordinator. What is TP Organics?. Only platform for organic, agro-ecological and low-input food and farming research

343 views • 16 slides

• Email Alert

论文  全文  图  表  新闻 

• Abstracting/Indexing
• Journal Metrics
• Current Editorial Board
• Early Career Advisory Board
• Previous Editor-in-Chief
• Past Issues
• Current Issue
• Special Issues
• Early Access
• Online Submission
• Information for Authors
• Share facebook twitter google linkedin

IEEE/CAA Journal of Automatica Sinica

• JCR Impact Factor: 11.8 , Top 4% (SCI Q1) CiteScore: 17.6 , Top 3% (Q1) Google Scholar h5-index: 77， TOP 5

Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies

Doi:  10.1109/jas.2021.1003925.

• Othmane Friha 1 ,  , 
• Mohamed Amine Ferrag 2 ,  , 
• Lei Shu 3, 4 ,  ,  , 
• Leandros Maglaras 5 ,  , 
• Xiaochan Wang 6 , 

Networks and Systems Laboratory, University of Badji Mokhtar-Annaba, Annaba 23000, Algeria

Department of Computer Science, Guelma University, Gulema 24000, Algeria

College of Engineering, Nanjing Agricultural University, Nanjing 210095, China

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

School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK

Department of Electrical Engineering, Nanjing Agricultural University, Nanjing 210095, China

Othmane Friha received the master degree in computer science from Badji Mokhtar-Annaba University, Algeria, in 2018. He is currently working toward the Ph.D. degree in the University of Badji Mokhtar-Annaba, Algeria. His current research interests include network and computer security, internet of things (IoT), and applied cryptography

Mohamed Amine Ferrag received the bachelor degree (June, 2008), master 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, NAULincoln Joint Research Center of Intelligent Engineering, Nanjing Agricultural University. His research interests include wireless network security, network coding security, and applied cryptography. He is featured in Stanford University’s list of the world’s Top 2% Scientists for the year 2019. 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 and 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 renowned 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 and Scopus) such as, IET Networks and International Journal of Internet Technology and Secured Transactions (Inderscience Publishers)

Lei Shu (M’07–SM’15) received the B.S. degree in computer science from South Central University for Nationalities 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, 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 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 (IoT). His current H-index is 54 and i10-index is 197 in Google Scholar Citation. His current research interests include wireless sensor networks and IoT. 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, WICON 2016, NCCA 2016, Chinacom 2017, InisCom 2017, WMNC 2017, and NCCA 2018

Leandros Maglaras (SM’15) received the B.Sc. degree from Aristotle University of Thessaloniki, Greece, in 1998, M.Sc. in industrial production and management from University of Thessaly in 2004, and M.Sc. and Ph.D. degrees in electrical & computer engineering from University of Volos in 2008 and 2014, respectively. He is the Head of the National Cyber Security Authority of Greece and a Visiting Lecturer in the School of Computer Science and Informatics at the De Montfort University, U.K. He serves on the Editorial Board of several International peer-reviewed journals such as IEEE Access , Wiley Journal on Security & Communication Networks , EAI Transactions on e-Learning and EAI Transactions on Industrial Networks and Intelligent Systems . He is an author of more than 80 papers in scientific magazines and conferences and is a Senior Member of IEEE. His research interests include wireless sensor networks and vehicular ad hoc networks

Xiaochan Wang is currently a Professor in the Department of Electrical Engineering at Nanjing Agricultural University. His main research fields include intelligent equipment for horticulture and intelligent measurement and control. He is an ASABE Member, and the Vice Director of CSAM (Chinese Society for Agricultural Machinery), and also the Senior Member of Chinese Society of Agricultural Engineering. He was awarded the Second Prize of Science and Technology Invention by the Ministry of Education (2016) and the Advanced Worker for Chinese Society of Agricultural Engineering (2012), and he also gotten the “Blue Project” in Jiangsu province young and middle-aged academic leaders (2010)

• Corresponding author: Lei Shu, e-mail: [email protected]
• Revised Date: 2020-11-25
• Accepted Date: 2020-12-30
• Agricultural internet of things (IoT) , 
• internet of things (IoT) , 
• smart agriculture , 
• smart farming , 
• sustainable agriculture

Proportional views

通讯作者: 陈斌, [email protected].

沈阳化工大学材料科学与工程学院 沈阳 110142

Figures( 12 )  /  Tables( 9 )

Article Metrics

• PDF Downloads( 2030 )
• Abstract views( 10582 )
• HTML views( 1385 )
• We review the emerging technologies used by the Internet of Things for the future of smart agriculture.
• We provide a classification of IoT applications for smart agriculture into seven categories, including, smart monitoring, smart water management, agrochemicals applications, disease management, smart harvesting, supply chain management, and smart agricultural practices.
• We provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward supply chain management based on the blockchain technology for agricultural IoTs.
• We highlight open research challenges and discuss possible future research directions for agricultural IoTs.
• Copyright © 2022 IEEE/CAA Journal of Automatica Sinica
• 京ICP备14019135号-24
• E-mail: [email protected]  Tel: +86-10-82544459, 10-82544746
• Address: 95 Zhongguancun East Road, Handian District, Beijing 100190, China

Export File

• Figure 1. The four agricultural revolutions
• Figure 2. Survey structure
• Figure 3. IoT-connected smart agriculture sensors enable the IoT
• Figure 4. The architecture of a typical IoT sensor node
• Figure 5. Fog computing-based agricultural IoT
• Figure 6. SDN/NFV architecture for smart agriculture
• Figure 7. Classification of IoT applications for smart agriculture
• Figure 8. Greenhouse system [ 101 ]
• Figure 9. Aerial-ground robotics system [ 67 ]
• Figure 10. Photovoltaic agri-IoT schematic diagram [ 251 ]
• Figure 11. Smart dairy farming system [ 254 ]
• Figure 12. IoT-based solar insecticidal lamp [ 256 ], [ 257 ]

Internet of Things and Analytics for Agriculture, Volume 3 pp 273–286 Cite as

Smart Farming with IoT: A Case Study

• Roopashree 5 ,
• Kanmani 5 ,
• Babitha 5 &
• Pavanalaxmi 5  
• First Online: 11 November 2021

835 Accesses

2 Citations

Part of the book series: Studies in Big Data ((SBD,volume 99))

The agriculture sector contributes a great role in the Indian economy. The major problem encountered by the farmer is low yield due to varying infrastructure, poor storage, inadequate access to the market, and timely delivery. The digital transformation is required to change traditional farming to a smart agriculture system using IoT. There are different areas in which IoT can be applied in the agricultural field such as to know the weather conditions, to observe and monitor the field, to analyze the crop health, planting, spraying the fertilizers, etc. By using a huge amount of data, accumulated by IoT smart sensors, the farmers can monitor the weather and soil condition. Sophisticated decisions can be taken to choose the precise fertilizers and pesticides to get a high yield. IoT-based smart farming improves the entire agriculture system by monitoring the field in real-time. Examinations are made on those sensor-empowered IoT frameworks that offer shrewd and keen types of assistance toward smart farming The objective of this chapter is to report a case study of the application of IoT in the agriculture field. Research papers on applications of IoT in the agriculture sector from different publishers are referred to prepare the chapter.

This is a preview of subscription content, log in via an institution .

Buying options

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Ferrández-Pastor, F.J., García-Chamizo, J.M., Nieto-Hidalgo, M., Mora-Pascual, J., Mora-Martínez, J.: Developing ubiquitous sensor network platform using internet of things: application in precision agriculture. Sensors 16 (7), 1141 (2016)

Article   Google Scholar  

Venkatesh, D., Tatti, M., Hardikar, P.G., Ahmed, S.S., Sharavana, K.: Cold storage management system for farmers based on IoT. Int. J. Recent Trends Eng. Res. 2455–1457 (2017)

Google Scholar  

Rao, R.N., Sridhar, B.: IoT based smart crop-field monitoring and automation irrigation system. In: 2018 2nd International Conference on Inventive Systems and Control (ICISC), pp. 478–483. IEEE (2018)

Dholu, M., Ghodinde, K.A.: Internet of things (IoT) for precision agriculture application. In: 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 339–342. IEEE (2018)

Sushanth, G., Sujatha, S.: IoT based smart agriculture system. In: 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 1–4. IEEE (2018)

Witjaksono, G., Rabih, A.A.S., Yahya, N., Alva, S.: IOT for agriculture: food quality and safety. In: IOP Conference Series: Materials Science and Engineering, vol. 343, no. 1, p. 012023. IOP Publishing (2018)

Hardyanto, R.H., Ciptadi, P.W.: Smart aquaponics design using internet of things technology. In: IOP Conference Series: Materials Science and Engineering, vol. 835, no. 1, p. 012026. IOP Publishing (2020)

Khoa, T.A., Man, M.M., Nguyen, T.Y., Nguyen, V., Nam, N.H.: Smart agriculture using IoT multi-sensors: a novel watering management system. J. Sens. Actuator Netw. 8 (3), 45 (2019)

Zhang, X., Zhang, J., Li, L., Zhang, Y., Yang, G.: Monitoring citrus soil moisture and nutrients using an IoT based system. Sensors 17 (3), 447 (2017)

Muangprathub, J., Boonnam, N., Kajornkasirat, S., Lekbangpong, N., Wanichsombat, A., Nillaor, P.: IoT and agriculture data analysis for smart farm. Comput. Electron. Agric. 156 , 467–474 (2019)

Burton, L., Dave, N., Fernandez, R.E., Jayachandran, K., Bhansali, S.: Smart gardening IoT soil sheets for real-time nutrient analysis. J. Electrochem. Soc. 165 (8), B3157 (2018)

Navulur, S., Prasad, M.G.: Agricultural management through wireless sensors and internet of things. Int. J. Elect. Comput. Eng. 7 (6), 3492 (2017)

Vaishali, S., Suraj, S., Vignesh, G., Dhivya, S., Udhayakumar, S.: Mobile integrated smart irrigation management and monitoring system using IOT. In: 2017 International Conference on Communication and Signal Processing (ICCSP), pp. 2164–2167. IEEE (2017)

Rajalakshmi, P., Mahalakshmi, S.D.: IoT based crop-field monitoring and irrigation automation. In: 2016 10th International Conference on Intelligent Systems and Control (ISCO), pp. 1–6. IEEE (2016)

Rawal, S.: IoT based smart irrigation system. Int. J. Comput. Appl. 159 (8), 7–11 (2017)

Thakur, D., Kumar, Y., Vijendra, S.: Smart irrigation and intrusions detection in agricultural fields using IoT. Procedia Comput. Sci. 167 , 154–162 (2020)

Saraf, S.B., Gawali, D.H.: IoT based smart irrigation monitoring and controlling system. In: 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 815–819. IEEE (2017)

Anandkumar, V., Kalaiarasan, T.R., Balakrishnan, S.: IoT based soil analysis and irrigation system. Int. J. Pure Appl. Math. 119 (12), 1127–1134 (2018)

Kamaruddin, F., Abd Malik, N.N.N., Murad, N.A., Latiff, N.M.A.A., Yusof, S.K.S., Hamzah, S.A.: IoT-based intelligent irrigation management and monitoring system using arduino. Telkomnika 17 (5), 2378–2388 (2019)

Pawar, S.B., Rajput, P., Shaikh, A.: Smart irrigation system using IOT and raspberry pi. Int. Res. J. Eng. Technol. 5 (8), 1163–1166 (2018)

Goap, A., Sharma, D., Shukla, A.K., Krishna, C.R.: An IoT based smart irrigation management system using Machine learning and open source technologies. Comput. Electron. Agric. 155 , 41–49 (2018)

Vij, A., Vijendra, S., Jain, A., Bajaj, S., Bassi, A., Sharma, A.: IoT and machine learning approaches for automation of farm irrigation system. Procedia Comput. Sci. 167 , 1250–1257 (2020)

Salvi, S., Jain, S.F., Sanjay, H.A., Harshita, T.K., Farhana, M., Jain, N., Suhas, M.V.: Cloud based data analysis and monitoring of smart multi-level irrigation system using IoT. In: 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 752–757. IEEE (2017)

Khan, A., Aziz, S., Bashir, M., Khan, M.U.: IoT and wireless sensor network based autonomous farming robot. In: 2020 International Conference on Emerging Trends in Smart Technologies (ICETST), pp. 1–5. IEEE (2020)

Gao, D., Sun, Q., Hu, B., Zhang, S.: A framework for agricultural pest and disease monitoring based on internet-of-things and unmanned aerial vehicles. Sensors 20 (5), 1487 (2020)

Bloise, N., Ruiz, M.C., D’Ambrosio, D., Guglieri, G.: Preliminary design of a remotely piloted aircraft system for crop-spraying on vineyards. In: 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 1–6. IEEE (2020)

Kim, J., Gadsden, S.A., Wilkerson, S.A.: A comprehensive survey of control strategies for autonomous quadrotors. Can. J. Electr. Comput. Eng. 43 (1), 3–16 (2019)

Download references

Author information

Authors and affiliations.

Sahyadri College of Engineering & Management (Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India), Adyar, Mangaluru, Karnataka, India

Roopashree,  Kanmani,  Babitha &  Pavanalaxmi

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Pavanalaxmi .

Editor information

Editors and affiliations.

School of Computer Engineering, KIIT University, Bhubaneswar, India

Prasant Kumar Pattnaik

Department of Computer Science and Engineering, GIET University, Gunupur, India

Raghvendra Kumar

Department of Computer Science and Engineering, Global Institute of Management and Technology, Nadia, West Bengal, India

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Cite this chapter.

Roopashree, Kanmani, Babitha, Pavanalaxmi (2022). Smart Farming with IoT: A Case Study. In: Pattnaik, P.K., Kumar, R., Pal, S. (eds) Internet of Things and Analytics for Agriculture, Volume 3. Studies in Big Data, vol 99. Springer, Singapore. https://doi.org/10.1007/978-981-16-6210-2_13

Download citation

DOI : https://doi.org/10.1007/978-981-16-6210-2_13

Published : 11 November 2021

Publisher Name : Springer, Singapore

Print ISBN : 978-981-16-6209-6

Online ISBN : 978-981-16-6210-2

eBook Packages : Engineering Engineering (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• How It Works
• Request a quote
• Get in touch

IoT in Agriculture: 9 Technology Use Cases for Smart Farming (and Challenges to Consider)

The article was updated on March 1, 2023.

With the growing adoption of the Internet of Things (IoT), connected devices have penetrated every aspect of our life , from health and fitness, home automation, automotive and logistics, to smart cities and industrial IoT.

Thus, it is only logical that IoT, connected devices, and automation would find its application in agriculture, and as such, tremendously improve nearly every facet of it. How could one still rely on horses and plows when self-driving cars and virtual reality are no longer a sci-fi fantasy but an everyday occurrence?

Farming has seen a number of technological transformations in the last decades, becoming more industrialized and technology-driven. By using various smart agriculture gadgets, farmers have gained better control over the process of raising livestock and growing crops, making it more predictable and improving its efficiency.

This, along with the growing consumer demand for agriculture products, has contributed to the increased proliferation of smart farming technologies worldwide. In 2022, the market share for IoT in agriculture reached $13.76 billion.

In this article, we will explore the IoT use cases in agriculture and examine their benefits. So, if you are considering investing into smart farming, or are planning to build an IoT solution for agriculture, dive right in.

• What is smart agriculture? The definition and market size

There are many ways to refer to modern agriculture. For example, AgriTech refers to the application of technology in agriculture in general.

Smart agriculture , on the other hand, is mostly used to denote the application of IoT solutions in agriculture. So what is smart agriculture using IoT? By using IoT sensors to collect environmental and machine metrics, farmers can make informed decisions, and improve just about every aspect of their work – from livestock to crop farming.

For example, by using smart agriculture sensors to monitor the state of crops, farmers can define exactly how many pesticides and fertilizers they have to use to reach optimal efficiency. The same applies to the smart farming definition.

Although smart agriculture IoT, as well as industrial IoT in general, aren’t as popular as consumer connected devices; yet the market is still very dynamic. The adoption of IoT solutions for agriculture is constantly growing.

Namely, COVID-19 has had a positive impact on IoT in the agriculture market share. Disruptions in the supply chain, and the shortage of qualified workers, has propelled its CAGR to 9,9%. In fact, as per recent reports, the smart framing market share is set to reach $28.56 billion by 2030.

At the same time, the global smart agriculture market size is expected to triple by 2025, reaching $15.3 billion (compared to being slightly over $5 billion back in 2016).

Because the market is still developing, there is still ample opportunity for businesses willing to join in. Building IoT products for agriculture within the coming years can set you apart as an early adopter, and as such, help you pave the way to success.

But why should you consider building an IoT application for agriculture in the first place?

The Benefits of smart farming: How’s IoT shaping agriculture

Technologies and IoT have the potential to transform agriculture in many aspects. Namely, there are 6 ways IoT can improve agriculture :

• Data, tons of data, collected by smart agriculture sensors, e.g. weather conditions, soil quality, crop’s growth progress or cattle’s health. This data can be used to track the state of your business in general as well as staff performance, equipment efficiency, etc.
• Better control over the internal processes and, as a result, lower production risks . The ability to foresee the output of your production allows you to plan for better product distribution. If you know exactly how much crops you are going to harvest, you can make sure your product won’t lie around unsold.
• Cost management and waste reduction thanks to the increased control over the production . Being able to see any anomalies in the crop growth or livestock health, you will be able to mitigate the risks of losing your yield.
• Increased business efficiency through process automation . By using smart devices, you can automate multiple processes across your production cycle, e.g. irrigation, fertilizing, or pest control.
• Enhanced product quality and volumes . Achieve better control over the production process and maintain higher standards of crop quality and growth capacity through automation.
• Reduced environmental footprint. Automation also carries environmental benefits. Smart farming technologies can cut down on the use of pesticides and fertilizer by offering more precise coverage, and thus, reduce greenhouse gas emissions.

As a result, all of these factors can eventually lead to higher revenue .

Now that we have outlined how IoT can be advantageously applied in the sphere of agriculture, let’s take a look at how the listed benefits can find their application in real life.

• IoT use cases in agriculture (with examples)

There are many types of IoT sensors for agriculture as well as IoT applications in agriculture in general:

1. Monitoring of climate conditions

Probably the most popular smart agriculture gadgets are weather stations, combining various smart farming sensors. Located across the field, they collect various data from the environment and send it to the cloud. The provided measurements can be used to map the climate conditions, choose the appropriate crops, and take the required measures to improve their capacity (i.e. precision farming).

Some examples of such agriculture IoT devices are allMETEO , Smart Elements , and Pycno .

2. Greenhouse automation

Typically, farmers use manual intervention to control the greenhouse environment. The use of IoT sensors enables them to get accurate real-time information on greenhouse conditions such as lighting, temperature, soil condition, and humidity.

In addition to sourcing environmental data, weather stations can automatically adjust the conditions to match the given parameters. Specifically, greenhouse automation systems use a similar principle.

For instance, Farmapp and Growlink are also IoT agriculture products offering such capabilities among others.

3. Crop management

One more type of IoT product in agriculture and another element of precision farming are crop management devices. Just like weather stations, they should be placed in the field to collect data specific to crop farming; from temperature and precipitation to leaf water potential and overall crop health.

Thus, you can monitor your crop growth and any anomalies to effectively prevent any diseases or infestations that can harm your yield. Arable and Semios can serve as good representations of how this use case can be applied in real life.

4. Cattle monitoring and management

Just like crop monitoring, there are IoT agriculture sensors that can be attached to the animals on a farm to monitor their health and log performance. Livestock tracking and monitoring help collect data on stock health, well-being, and physical location.

For example, such sensors can identify sick animals so that farmers can separate them from the herd and avoid contamination. Using drones for real-time cattle tracking also helps farmers reduce staffing expenses. This works similarly to IoT devices for petcare .

For example, SCR by Allflex and Cowlar use smart agriculture sensors (collar tags) to deliver temperature, health, activity, and nutrition insights on each individual cow as well as collective information about the herd.

5. Precision farming

Also known as precision agriculture, precision farming is all about efficiency and making accurate data-driven decisions. It’s also one of the most widespread and effective applications of IoT in agriculture.

By using IoT sensors, farmers can collect a vast array of metrics on every facet of the field microclimate and ecosystem: lighting, temperature, soil condition, humidity, CO2 levels, and pest infections. This data enables farmers to estimate optimal amounts of water, fertilizers, and pesticides that their crops need, reduce expenses, and raise better and healthier crops.

For example, CropX builds IoT soil sensors that measure soil moisture, temperature, and electric conductivity enabling farmers to approach each crop’s unique needs individually. Combined with geospatial data, this technology helps create precise soil maps for each field. Mothive offers similar services, helping farmers reduce waste, improve yields, and increase farm sustainability.

6. Agricultural drones

Perhaps one of the most promising agritech advancements is the use of agricultural drones in smart farming. Also known as UAVs (unmanned aerial vehicles), drones are better equipped than airplanes and satellites to collect agricultural data. Apart from surveillance capabilities, drones can also perform a vast number of tasks that previously required human labor: planting crops, fighting pests and infections, agriculture spraying, crop monitoring, etc.

Read more: Why Use Agriculture Drones? Main Benefits and Best Practices

DroneSeed , for example, builds drones for planting trees in deforested areas. The use of such drones is 6 times more effective than human labor. A Sense Fly agriculture drone eBee SQ uses multispectral image analyses to estimate the health of crops and comes at an affordable price.

7. Predictive analytics for smart farming

Precision agriculture and predictive data analytics go hand in hand. While IoT and smart sensor technology are a goldmine for highly relevant real-time data, the use of data analytics helps farmers make sense of it and come up with important predictions: crop harvesting time, the risks of diseases and infestations, yield volume, etc. Data analytics tools help make farming, which is inherently highly dependent on weather conditions, more manageable, and predictable.

For example, the Crop Performance platform helps farmers access the volume and quality of yields in advance, as well as their vulnerability to unfavorable weather conditions, such as floods and drought. It also enables farmers to optimize the supply of water and nutrients for each crop and even select yield traits to improve quality.

Applied in agriculture, solutions like SoilScout enable farmers to save up to 50% irrigation water, reduce the loss of fertilizers caused by overwatering, and deliver actionable insights regardless of season or weather conditions.

8. End-to-end farm management systems

A more complex approach to IoT products in agriculture can be represented by the so-called farm productivity management systems. They usually include a number of agriculture IoT devices and sensors, installed on the premises as well as a powerful dashboard with analytical capabilities and in-built accounting/reporting features.

This offers remote farm monitoring capabilities and allows you to streamline most of the business operations. Similar solutions are represented by FarmLogs and Cropio .

In addition to the listed IoT agriculture use cases, some prominent opportunities include vehicle tracking (or even automation), storage management, logistics, etc.

9. Robots and autonomous machines

Robotic innovations also offer a promising future in the field of autonomous machines for agricultural purposes. Some farmers already use automated harvesters, tractors, and other machines and vehicles that can operate without a human controlling it. Such robots can complete repetitive, challenging, and labor-intensive tasks.

For instance, modern agrobots include automated tractors that can work on assigned routes, send notifications, start work at planned hours, etc. Such tractors are driverless and cut farmers’ labor costs. Bear Flag Robotics is one company that works on such technology at the moment.

In addition, smart farming also uses robots for planting seeds, weeding, and watering. The given jobs are very demanding and labor-intensive. Yet, robots, such as ones from Eco Robotics , can detect weeds or plant seeds using computer vision and AI technology. These agricultural robots work delicately, drastically reducing harm to the plants and the environment.

• Things to consider before developing your smart farming solution

As we can see, the use cases for IoT in agriculture are endless. There are many ways smart devices can help you increase your farm’s performance and revenue. However, agriculture IoT apps development is no easy task.

There are certain challenges you need to be aware of if you are considering investing into smart farming.

1. The hardware

To build an IoT solution for agriculture, you need to choose the sensors for your device (or create a custom one). Your choice will depend on the types of information you want to collect and the purpose of your solution in general.

In any case, the quality of your sensors is crucial to the success of your product: it will depend on the accuracy of the collected data and its reliability.

2. The brain

Data analytics should be at the core of every smart agriculture solution. The collected data itself will be of little help if you cannot make sense of it.

Thus, you need to have powerful data analytics capabilities and apply predictive algorithms and machine learning in order to obtain actionable insights based on the collected data.

3. The maintenance

Maintenance of your hardware is a challenge that is of primary importance for IoT products in agriculture, as the sensors are typically used in the field and can be easily damaged.

Thus, you need to make sure your hardware is durable and easy to maintain. Otherwise you will need to replace your sensors more often than you would like.

4. The mobility

Smart farming applications should be tailored for use in the field. A business owner or farm manager should be able to access the information on site or remotely via a smartphone or desktop computer.

Plus, each connected device should be autonomous and have enough wireless range to communicate with the other devices and send data to the central server.

Thank you for downloading the guide!

Free guide to your project budgeting.

Explore real-life examples and discover the ways to stay within your budget.

5. The infrastructure

To ensure that your smart farming application performs well (and to make sure it can handle the data load), you need a solid internal infrastructure.

Furthermore, your internal systems have to be secure. Failing to properly secure your system only increases the likeliness of someone breaking into it, stealing your data or even taking control of your autonomous tractors.

6. Connectivity

The need to transmit data between many agricultural facilities still poses a challenge for the adoption of smart farming. Needless to say, the connection between these facilities should be reliable enough to withstand bad weather conditions and to ensure non-disruptive operations.

Today, IoT devices still use varying connection protocols, although the efforts to develop unified standards in this area are currently underway. The advent of 5G and technologies like space-based Internet will, hopefully, help find a solution to this problem.

7. Data collection frequency

Because of the high variety of data types in the agricultural industry, ensuring the optimal data collection frequency can be problematic. The data from field-based, aerial and environmental sensors, apps, machinery, and equipment, as well as processed analytical data, can be a subject of restriction and regulations.

Today, the safe and timely delivery, and sharing of this data is one of the current smart farming challenges.

8. Data security in the agriculture industry

Precision agriculture and IoT technology imply working with large sets of data, which increases the number of potential security loopholes that perpetrators can use for data theft and hacking attacks. Unfortunately, data security in agriculture is still, to a large extent, an unfamiliar concept.

Many farms, for example, use drones that transmit data to farm machinery. This machinery connects to the Internet but has little to zero security protection, such as user passwords or remote access authentications.

Some of the basic IoT security recommendations include monitoring data traffic, using encryption methods to protect sensitive data, leveraging AI-based security tools to detect traces of suspicious activity in real-time, and storing data in the blockchain to ensure its integrity.

To fully benefit from IoT, farmers will have to get familiar with the data security concept, set up internal security policies, and adhere to them.

• Our work case of IoT solutions for agriculture

Our team at Eastern Peak has also contributed to the progress of IoT applications in agriculture. The IoT-powered irrigation application, GreenIQ, helps gardeners reduce water usage by 50%, monitor humidity levels, and predict the best timing for irrigation. GreenIQ uses smart sensors to analyze meteorological conditions and soil types, creating the perfect irrigation strategy and adapting to new environments.

The GreenIQ application also integrates with the most well-known home automation platforms. This app is another valuable contribution to eco-friendly gardening and one of many examples of how smart farming products can change the future of agriculture.

• Grow your agriculture business with smart IoT solutions from Eastern Peak

According to the UN Food and Agriculture Organization (FAO) , the global population is expected to surpass 9 billion people by 2050. To produce enough food for the given population, agriculture production volumes have to increase by 50%.

As the resources for agricultural operations are limited (most of the lands suitable for farming are already in use), the only way to increase volume is to improve production efficiency. There is no doubt as to the extent with which smart farming can help tackle this challenge; in fact, it seems that it is not possible without it. Here at Eastern Peak we develop custom IoT solutions for agriculture, tailored to your particular needs.

How to get started?

From cattle tracking to advanced field mapping, IoT applications in smart agriculture vary from farm to farm depending on your market segment, climate, and region. In many instances, out-of-the-box tools won’t be relevant, and you may need a tailored smart farming IoT solution. At Eastern Peak we approach each customer individually to meet their unique needs.

The product discovery phase is the best first step you can take to lay a solid foundation for the development of your app. It includes a functional specification, UX/UI design, and a visual prototype that will give you a clear vision of the end product. On average, this phase takes 4-6 weeks.

The product discovery phase can help you:

• define a full scope of work and develop a roadmap for the project
• set a realistic budget for your MVP and plan your resources
• test the waters with your audience using a visual prototype
• craft a convincing investment pitch
• get to know your team

We at Eastern Peak have already helped many startups and Fortune 500 companies digitize and streamline their operations with the help of technologies. We provide end-to-end services building IoT solutions across a number of business domains, from hardware design to software development, testing, and integration.

To receive professional consultation from our experts, get in touch with us using our contact form .

• Smart Farming: How Automation Is Transforming Agriculture
• 3 Edge Computing Use Cases for Smart Farming

Smart Agriculture Monitoring Solutions to Optimize Farming Productivity

• 6 Cool Examples of Internet of Things Applications and How to Develop One

About the author:

Alexey Shalimov, CEO at Eastern Peak

As CEO at Eastern Peak, a professional software consulting and development company, Alexey ensures top quality and cost-effective services to clients from all over the world. Alexey is also a founder and technology evangelist at several technology companies. Previously, as a CEO of the Gett (GetTaxi) technology company, Alexey was in charge of developing the revolutionary Gett service from ground up and deploying the operation across the globe from New York to London and Tel Aviv.

• The Benefits of smart farming: How’s IoT shaping agriculture

Enjoyed the read?

Don't miss our next article!

MORE ARTICLES

13 Cool Examples of Internet of Things Applications and How to Develop One

The article was updated on February 28, 2023. The number of IoT-connected devices is predicted to triple in just a ...

The article was updated on March 2, 2023. Of all the IoT-enabled solutions, smart agriculture systems unmistakably stand out. Being ...

Cookies help us enhance your experience and navigation. By continuing to browse, you agree to the storing of cookies on your device. We do not collect your personal information unless you explicitly ask us to do so. Please see our Privacy policy for more details.

American Society for Microbiology

• Case Studies
• A Resistant Foe in Medicine and Agriculture

Case Studies A Resistant Foe in Medicine and Agriculture

April 18, 2024

Submit a Case Study Presentation

A 71-year-old male develops acute respiratory distress 4 weeks after a bone marrow transplant. The patient experiences fever, tachycardia and worsening mentation. Blood and respiratory cultures reveal a gram-negative bacillus, which was not identified by rapid respiratory and blood culture molecular panels. The microorganism is a non-lactose fermenter, and colonies are smooth and slightly raised on blood agar. There is also a notable dirt-like smell. 

Can you guess the diagnosis?

Return to this page on Mon., April 22 for the reveal!

Author Information

Asm microbe 2024 registration now open, discover asm membership, get published in an asm journal.