Abstract
Accurate weather prediction and effective agricultural risk management are essential to improve the productivity and sustainability of the farm sector in India. However, extreme climate fluctuations and weather uncertainty pose significant challenges for farmers and policymakers. Artificial Intelligence (AI) offers a potential solution to this problem with its ability to analyze complex data and identify patterns. This study aims to explore the application of Artificial Intelligence in weather prediction and agricultural risk management in India. Specifically, the study seeks to develop AI models that accurately predict weather and recommend appropriate agrarian risk management strategies. In this study, historical weather data, climate data, and agricultural data were collected from various sources. Various AI techniques, such as machine learning, deep learning, and natural language processing, are used to analyze data and develop weather prediction and agricultural risk management models. The model is then validated and optimized using test data. The results showed that the developed AI model can predict the weather more accurately than conventional methods. The model can also provide specific recommendations for agricultural risk management, such as proper crop selection, optimal planting timing, and other risk mitigation strategies. This research shows the vast potential of Artificial Intelligence in improving weather prediction and agricultural risk management in India. By adopting AI technology, farmers and policymakers can make better decisions and improve the productivity and sustainability of the agricultural sector.
Full text article
References
Abol-Fotouh, D., Dörling, B., Zapata-Arteaga, O., Rodríguez-Martínez, X., Gómez, A., Reparaz, J. S., Laromaine, A., Roig, A., & Campoy-Quiles, M. (2019). Farming thermoelectric paper. Energy & Environmental Science, 12(2), 716–726. https://doi.org/10.1039/C8EE03112F
Afridi, M. S., Ali, S., Salam, A., César Terra, W., Hafeez, A., Sumaira, Ali, B., S. AlTami, M., Ameen, F., Ercisli, S., Marc, R. A., Medeiros, F. H. V., & Karunakaran, R. (2022). Plant Microbiome Engineering: Hopes or Hypes. Biology, 11(12), 1782. https://doi.org/10.3390/biology11121782
Alavaisha, E., Manzoni, S., & Lindborg, R. (2019). Different agricultural practices affect soil carbon, nitrogen and phosphorous in Kilombero -Tanzania. Journal of Environmental Management, 234, 159–166. https://doi.org/10.1016/j.jenvman.2018.12.039
Attia, Z. I., Noseworthy, P. A., Lopez-Jimenez, F., Asirvatham, S. J., Deshmukh, A. J., Gersh, B. J., Carter, R. E., Yao, X., Rabinstein, A. A., Erickson, B. J., Kapa, S., & Friedman, P. A. (2019). An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction. The Lancet, 394(10201), 861–867. https://doi.org/10.1016/S0140-6736(19)31721-0
Avgoustaki, D. D., & Xydis, G. (2020). Plant factories in the water-food-energy Nexus era: A systematic bibliographical review. Food Security, 12(2), 253–268. https://doi.org/10.1007/s12571-019-01003-z
Beacham, A. M., Vickers, L. H., & Monaghan, J. M. (2019). Vertical farming: A summary of approaches to growing skywards. The Journal of Horticultural Science and Biotechnology, 94(3), 277–283. https://doi.org/10.1080/14620316.2019.1574214
Beavers, A. W., Kennedy, A. O., Blake, J. P., & Comstock, S. S. (2024). Development and evaluation of food preservation lessons for gardeners: Application of the DESIGN process. Public Health Nutrition, 27(1), e23. https://doi.org/10.1017/S1368980023002926
Deng, S., Zhao, H., Fang, W., Yin, J., Dustdar, S., & Zomaya, A. Y. (2020). Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence. IEEE Internet of Things Journal, 7(8), 7457–7469. https://doi.org/10.1109/JIOT.2020.2984887
Dwivedi, Y. K. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57(Query date: 2024-05-23 12:51:03). https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Fountas, S., Mylonas, N., Malounas, I., Rodias, E., Hellmann Santos, C., & Pekkeriet, E. (2020). Agricultural Robotics for Field Operations. Sensors, 20(9), 2672. https://doi.org/10.3390/s20092672
Goel, R. K., Yadav, C. S., Vishnoi, S., & Rastogi, R. (2021). Smart agriculture – Urgent need of the day in developing countries. Sustainable Computing: Informatics and Systems, 30, 100512. https://doi.org/10.1016/j.suscom.2021.100512
Jägermeyr, J. (2020). Agriculture’s Historic Twin-Challenge Toward Sustainable Water Use and Food Supply for All. Frontiers in Sustainable Food Systems, 4, 35. https://doi.org/10.3389/fsufs.2020.00035
Jellason, N. P., Robinson, E. J. Z., & Ogbaga, C. C. (2021). Agriculture 4.0: Is Sub-Saharan Africa Ready? Applied Sciences, 11(12), 5750. https://doi.org/10.3390/app11125750
Kim, J. H., Jobbágy, E. G., Richter, D. D., Trumbore, S. E., & Jackson, R. B. (2020). Agricultural acceleration of soil carbonate weathering. Global Change Biology, 26(10), 5988–6002. https://doi.org/10.1111/gcb.15207
Kumar, A., Subrahmanyam, G., Mondal, R., Cabral-Pinto, M. M. S., Shabnam, A. A., Jigyasu, D. K., Malyan, S. K., Fagodiya, R. K., Khan, S. A., Kumar, A., & Yu, Z.-G. (2021). Bio-remediation approaches for alleviation of cadmium contamination in natural resources. Chemosphere, 268, 128855. https://doi.org/10.1016/j.chemosphere.2020.128855
Kuska, M. T., Heim, R. H. J., Geedicke, I., Gold, K. M., Brugger, A., & Paulus, S. (2022). Digital plant pathology: A foundation and guide to modern agriculture. Journal of Plant Diseases and Protection, 129(3), 457–468. https://doi.org/10.1007/s41348-022-00600-z
Lan, Z., Zhang, G., Chen, X., Zhang, Y., Zhang, K. A. I., & Wang, X. (2019). Reducing the Exciton Binding Energy of Donor–Acceptor?Based Conjugated Polymers to Promote Charge?Induced Reactions. Angewandte Chemie International Edition, 58(30), 10236–10240. https://doi.org/10.1002/anie.201904904
Leng, G., & Hall, J. (2019). Crop yield sensitivity of global major agricultural countries to droughts and the projected changes in the future. Science of The Total Environment, 654, 811–821. https://doi.org/10.1016/j.scitotenv.2018.10.434
Popkova, E. G. (2022). Vertical Farms Based on Hydroponics, Deep Learning, and AI as Smart Innovation in Agriculture. Dalam E. G. Popkova & B. S. Sergi (Ed.), Smart Innovation in Agriculture (Vol. 264, hlm. 257–262). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-7633-8_28
Rodrigues, C. G., Garcia, B. F., Verdegem, M., Santos, M. R., Amorim, R. V., & Valenti, W. C. (2019). Integrated culture of Nile tilapia and Amazon river prawn in stagnant ponds, using nutrient-rich water and substrates. Aquaculture, 503, 111–117. https://doi.org/10.1016/j.aquaculture.2018.12.073
Rose, D. C., Wheeler, R., Winter, M., Lobley, M., & Chivers, C.-A. (2021). Agriculture 4.0: Making it work for people, production, and the planet. Land Use Policy, 100, 104933. https://doi.org/10.1016/j.landusepol.2020.104933
Sedeek, K. E. M., Mahas, A., & Mahfouz, M. (2019). Plant Genome Engineering for Targeted Improvement of Crop Traits. Frontiers in Plant Science, 10, 114. https://doi.org/10.3389/fpls.2019.00114
SharathKumar, M., Heuvelink, E., & Marcelis, L. F. M. (2020). Vertical Farming: Moving from Genetic to Environmental Modification. Trends in Plant Science, 25(8), 724–727. https://doi.org/10.1016/j.tplants.2020.05.012
Sharma, P., & Kumar, S. (2021). Bioremediation of heavy metals from industrial effluents by endophytes and their metabolic activity: Recent advances. Bioresource Technology, 339, 125589. https://doi.org/10.1016/j.biortech.2021.125589
Shen, N., Wang, T., Gan, Q., Liu, S., Wang, L., & Jin, B. (2022). Plant flavonoids: Classification, distribution, biosynthesis, and antioxidant activity. Food Chemistry, 383, 132531. https://doi.org/10.1016/j.foodchem.2022.132531
Soni, N. (2020). Artificial Intelligence in Business: From Research and Innovation to Market Deployment. Procedia Computer Science, 167(Query date: 2024-05-23 12:51:03), 2200–2210. https://doi.org/10.1016/j.procs.2020.03.272
Soullier, G., Demont, M., Arouna, A., Lançon, F., & Mendez Del Villar, P. (2020). The state of rice value chain upgrading in West Africa. Global Food Security, 25, 100365. https://doi.org/10.1016/j.gfs.2020.100365
Sun, X., Zhong, T., Zhang, L., Zhang, K., & Wu, W. (2019). Reducing ammonia volatilization from paddy field with rice straw derived biochar. Science of The Total Environment, 660, 512–518. https://doi.org/10.1016/j.scitotenv.2018.12.450
Ting, D. S. W., Pasquale, L. R., Peng, L., Campbell, J. P., Lee, A. Y., Raman, R., Tan, G. S. W., Schmetterer, L., Keane, P. A., & Wong, T. Y. (2019). Artificial intelligence and deep learning in ophthalmology. British Journal of Ophthalmology, 103(2), 167–175. https://doi.org/10.1136/bjophthalmol-2018-313173
Tudi, M., Daniel Ruan, H., Wang, L., Lyu, J., Sadler, R., Connell, D., Chu, C., & Phung, D. T. (2021). Agriculture Development, Pesticide Application and Its Impact on the Environment. International Journal of Environmental Research and Public Health, 18(3), 1112. https://doi.org/10.3390/ijerph18031112
Tuomisto, H. L. (2019). Vertical Farming and Cultured Meat: Immature Technologies for Urgent Problems. One Earth, 1(3), 275–277. https://doi.org/10.1016/j.oneear.2019.10.024
Vásquez, Z. S., De Carvalho Neto, D. P., Pereira, G. V. M., Vandenberghe, L. P. S., De Oliveira, P. Z., Tiburcio, P. B., Rogez, H. L. G., Góes Neto, A., & Soccol, C. R. (2019). Biotechnological approaches for cocoa waste management: A review. Waste Management, 90, 72–83. https://doi.org/10.1016/j.wasman.2019.04.030
Wang, X., Shao, S., & Li, L. (2019). Agricultural inputs, urbanization, and urban-rural income disparity: Evidence from China. China Economic Review, 55, 67–84. https://doi.org/10.1016/j.chieco.2019.03.009
Zambon, I., Cecchini, M., Egidi, G., Saporito, M. G., & Colantoni, A. (2019). Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs. Processes, 7(1), 36. https://doi.org/10.3390/pr7010036
Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing. Proceedings of the IEEE, 107(8), 1738–1762. https://doi.org/10.1109/JPROC.2019.2918951
Authors
Copyright (c) 2024 Zargari Benjamin, Topacio Najmeh, Mashhadi Shariati

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.