Deep Learning and Internet of Things Integrated Farming during COVID-19 in India
Abhishek P.1Ramesh V.1
Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya University, Kanchipuram, Tamil Nadu, India
Background and Aim of Study: Deep learning and Internet of things (IoT) technologies have great potential for their application in various fields, including agriculture. Agriculture is a central pillar of the Indian economy. Agriculture is largest livelihood provider in India. Agriculture employed more than 50% of the Indian work force and contributed 17–18% to country’s GDP. Indian agriculture sector has been facing several challenges because of COVID-19 restrictions. Outbreak of corona virus in India and the consequent lockdown, unfortunately, also coincided with the country’s peak harvesting time of a variety of crops of the season. Across India, a massive agricultural crisis is due to COVID-19 shutdown. The aim of the study: to explore the possibilities of using Deep learning and IoT technologies as a tool to handle many problems in agriculture domain such as lack of irrigation infrastructure, market infrastructure and transport infrastructure etc. Materials and Methods: We have studied various problems faced by Indian farmers during this lockdown and various steps taken by Indian government to tackle this global pandemic of COVID-19. This study introduced possible solutions for improvement by using Deep learning based Internet of things ecosystem that helps in gathering information from farmers such location-based information, crop health information and environmental constraints. Results: We proposed an IoT based agriculture framework to monitor and analyse crop health by using Deep learning remotely. This framework promotes a fast development of agricultural modernization, realize smart agriculture and effectively solve the problems concerning agriculture. Our research findings indicate that Deep learning provides high accuracy, outperforming existing commonly used data processing techniques. Conclusions: Data-driven agriculture, with the help of internet of things and Deep learning techniques, sets the grounds for the sustainable agriculture of the future. This study proposed the future advanced farm management systems through Deep learning and IoT technologies to solve various problem faced by Indian farmers during COVID-19 pandemic.
Deep learning, IoT, COVID-19, irrigation infrastructure, data driven agriculture
Abbasi, M., Yaghmaee, M. H., & Rahnama, F. (2019). Internet of Things in agriculture: A survey. 3rd International Conference on Internet of Things and Applications (IoT), 17–28. doi:10.1109/IICITA.2019.8808839 Retrieved from
Ayaz, M., Ammad-Uddin, M., Sharif, Z., Mansour, A., & Aggoune, E. M. (2019). Internet-Of-Things (IoT)-based smart agriculture: Toward making the fields talk. IEEE Access: Vol. 7. Special Section on New Technologies for Smart Farming 4.0: Research Challenges and Opportunities (pp. 129551–129583). doi:10.1109/ACCESS.2019.2932609
DaCosta, F., & Henderson, B. (2013). Rethinking the Internet of Things. Berkeley, CA: Apress. doi:10.1007/978-1-4302-5741-7
Das, R. K., Manisha, P., & Dash, S. S. (2019). Smart agriculture system in India using internet of things. In J. Nayak, A. Abraham, B. M. Krishna, G. T. Chandra Sekhar, & A. K. Das (Eds.), Soft Computing in Data Analytics: Vol. 758. Advances in Intelligent Systems and Computing (pp. 247–255). Singapore: Springer. doi:10.1007/978-981-13-0514-6_25
Disha, G., Khan, S., & Alam, M. (2020). Integrative use of IoT and deep learning for agricultural applications. In P. Singh, B. Panigrahi, N. Suryadevara, S. Sharma, A. Singh (Eds.), Proceedings of ICETIT 2019: Vol. 605. Lecture Notes in Electrical Engineering (pp. 521–531). Cham: Springer. doi:10.1007/978-3-030-30577-2_46
Farooq, M. S., Riaz, S., Abid, A., Abid, K., & Naeem, M. A. (2019). A Survey on the role of IoT in agriculture for the implementation of smart farming. IEEE Access: Vol. 7. Special Section on New Technologies for Smart Farming 4.0: Research Challenges and Opportunities (pp. 156237-156271). doi:10.1109/ACCESS.2019.2949703
Foughali, K., Fathallah, K., & Frihida, A. (2018). Using cloud IOT for disease prevention in precision agriculture. Procedia Computer Science, 130, 575–582. doi:10.1016/j.procs.2018.04.106
Jaiswal, S. P., Bhadoria, V. S., Agrawal, A., & Ahuja, H. (2019). Internet of Things (IoT) for smart agriculture and farming in developing nations. International Journal of Scientific & Technology Research, 8(12), 1049–1056.
Israni S., Meharkure, H., & Yelore, P. (2015). Application of IOT Based System for Advance Agriculture in India. International Journal of Innovative Research in Computer and Communication Engineering, 3(11), 10831–10837. doi:10.15680/IJIRCCE.2015.0311099
Kareemulla, K., Ramasundaram, P., Kumar, S., & RamaRao, C. A. (2013). Impact of national rural employment guarantee scheme in India on rural poverty and food security. Current Agriculture Research Journal, 1(1), 13–28. doi:10.12944/CARJ.1.1.02
Karunakanth, M., Venkatesan, R., Jaspher, G., & Kathrine, W. (2018). IoT based smart irrigation system for home based organic garden. International Journal of Pure and Applied Mathematics,119(12), 16193–16199.
Kumar, A., Padhee, A. K., & Kumar, S. (2020). How Indian agriculture should change after COVID-19. Food Security, 1–4. Advance online publication. doi:10.1007/s12571-020-01063-6
Maduranga, M.W.P., & Abeysekera, R. (2020). Machine learning applications in IoT based agriculture and smart farming: A review. International Journal of Engineering Applied Sciences and Technology, 4(12), 24–27. doi:10.33564/ijeast.2020.v04i12.004
Melnyk, Yu. B., & Pypenko, I. S. (2018). Training of future specialists in higher education institutions. International Journal of Science Annals, 1(1-2), 4–11. doi:10.26697/ijsa.2018.1-2.01
Navarro, E., Costa, N., & Pereira, A. (2020). A systematic review of IoT Solutions for smart farming. Sensors, 20(15), 4231. doi:10.3390/s20154231
Patil, V. C., Al-Gaadi, K. A., Biradar, D. P., & Rangaswamy, M. (2012). Internet of Things (IoT) and cloud computing for agriculture: An overview. Proceedings of Agro-Informatics and Precision Agriculture, 292–296.
Ponraj, A. S., & Vigneswaran, T. (2019). Machine learning approach for agricultural IoT. International Journal of Recent Technology and Engineering 7(6), 383–392.
Rajeswari, S., Suthendran, K., & Rajakumar, K. (2017). A smart agricultural model by integrating IoT, mobile and cloud-based big data analytics. International Conference on Intelligent Computing and Control (I2C2), 1–5, doi:10.1109/I2C2.2017.8321902
Ramya, S., Swetha, A. M., & Doraipandian, M. (2020). IoT framework for smart irrigation using machine learning technique. Journal of Computer Science, 16(3), 355–363, doi:10.3844/jcssp.2020.355.363
Raju, K. L., & Vijayaraghavan, V. (2020). IoT technologies in agricultural environment: A survey. Wireless Personal Communications, 4, 2415–2446.
Ray, P. P. (2017). Internet of things for smart agriculture: Technologies, practices and future direction. Journal of Ambient Intelligence and Smart Environments, 9, 395–420, doi:10.3233/AIS-170440
Roham, V. S., Pawar, G. A., Patil, A. S., & Rupnar, P. R. (2015). Smart farm using wireless sensor network. IJCA Proceedings on National Conference on Advances in Computing, 6, 8–11. Retrieved from
Suba, G., Jagadeesh, Y., Karthik, S., & Sampath, E. (2015). Smart irrigation system through wireless sensor networks. ARPN Journal of Engineering and Applied Sciences, 10(17), 7452–7455.
Information about the author:
Abhishek Pandey –; PhD Research Scholar, Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya University, Kanchipuram, Tamil Nadu, India.
Ramesh Vamanan –; Assistant Professor, Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya University, Kanchipuram, Tamil Nadu, India.
Cite this article as: 
Abhishek, P., & Ramesh, V. (2020). Deep Learning and Internet of Things Integrated Farming during COVID-19 in India. International Journal of Education and Science, 3(3), 10–18. doi:10.26697/ijes.2020.3.2

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