5 Ways AI Is Making Farming Smarter and Greener
DOI:
https://doi.org/10.5281/zenodo.21531583Keywords:
Artificial intelligence, Precision agriculture, Smart irrigation, Sustainable farmingAbstract
Artificial intelligence (AI) is transforming agriculture by enabling smarter, more precise and environmentally sustainable farming practices. This article highlights five major applications of AI in agriculture: weather and climate-risk forecasting, precision irrigation, early detection of pests and diseases, optimized fertilizer and pesticide use, and mobile-based advisory services. By integrating satellite imagery, sensors, drones, machine learning, Internet of Things devices and AI-powered chatbots, farmers can make timely decisions, reduce input waste, conserve water and minimize crop losses. However, limited internet connectivity, inadequate rural infrastructure, low digital literacy, data-security concerns and unequal access remain significant barriers. Addressing these challenges through inclusive policies, farmer training and responsible technology development will be essential for ensuring that AI complements farmers’ knowledge and supports resilient, productive and sustainable agriculture.
References
Araújo, S. O., Peres, R. S., Barata, J., Lidon, F., & Ramalho, J. C. (2021). Characterising the agriculture 4.0 landscape—emerging trends, challenges and opportunities. Agronomy, 11(4), 667.
Arshdeep Singh, A. S., Arun Kumar, A. K., Anita Jaswal, A. J., Maninder Singh, M. S., & Gaikwad, D. S. (2018). Nutrient use efficiency concept and interventions for improving nitrogen use efficiency.
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human resource management review, 33(1), 100899.
Das, S., Dash, S., & Banerjee, P. K. (2023). Relationship between farmers' profiles with their attitude towards use of Kisan Call Centre. Guj. J. Ext. Edu, 35(2), 104–107.
Dursun, M. (2011). A wireless application of drip irrigation automation supported by soil moisture sensors. Scientific Research and Essays.
Gliever, C., & Slaughter, D. C. (2001). Crop versus weed recognition with artificial neural networks. In ASAE meeting paper.
Jarial, S. (2023). Internet of Things application in Indian agriculture, challenges and effect on the extension advisory services--a review. Journal of Agribusiness in Developing and Emerging Economies, 13(4), 505–519.
Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced agrochem, 2(1), 15–30.
Kalaivani, T., Allirani, A., & Priya, P. (2011). A survey on Zigbee based wireless sensor networks in agriculture. In 3rd international conference on Trendz in information sciences & computing (TISC2011) (pp. 85–89).
Lee, S., & Yun, C. M. (2023). A deep learning model for predicting risks of crop pests and diseases from sequential environmental data. Plant Methods, 19(1), 145.
Maier, H. R., & Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental modelling & software, 15(1), 101–124.
Mohsan, S. A. H., Othman, N. Q. H., Li, Y., Alsharif, M. H., & Khan, M. A. (2023). Unmanned aerial vehicles (UAVs): Practical aspects, applications, open challenges, security issues, and future trends. Intelligent service robotics, 16(1), 109–137.
Mostaco, G. M., De Souza, I. R. C., Campos, L. B., Cugnasca, C. E., & others. (2018). AgronomoBot: a smart answering Chatbot applied to agricultural sensor networks. In 14th international conference on precision agriculture (Vol. 24, pp. 1–13).
Niranjan, P. Y., Rajpurohit, V. S., & Malgi, R. (2019). A survey on chatbot system for agriculture domain. In 2019 1st International Conference on Advances in Information Technology (ICAIT) (pp. 99–103).
Nyaga, J. M., Onyango, C. M., Wetterlind, J., & Söderström, M. (2021). Precision agriculture research in sub-Saharan Africa countries: A systematic map. Precision Agriculture, 22(4), 1217–1236.
Owebor, K., Diemuodeke, O. E., Briggs, T. A., Eyenubo, O. J., Ogorure, O. J., & Ukoba, M. O. (2022). Multi-criteria optimisation of integrated power systems for low-environmental impact. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 44(2), 3459–3476.
Rajak, P., Ganguly, A., Adhikary, S., & Bhattacharya, S. (2023). Internet of Things and smart sensors in agriculture: Scopes and challenges. Journal of Agriculture and Food Research, 14, 100776.
Shoaib, M., Shah, B., Ei-Sappagh, S., Ali, A., Ullah, A., Alenezi, F., et al. (2023). An advanced deep learning models-based plant disease detection: A review of recent research. Frontiers in plant science, 14, 1158933.
Song, H., & He, Y. (2005). Crop nutrition diagnosis expert system based on artificial neural networks. In Third international conference on information technology and applications (ICITA'05) (Vol. 1, pp. 357–362).
Symeonaki, E., Arvanitis, K., Piromalis, D., & Papoutsidakis, M. (2019). Conversational user interface integration in controlling IoT devices applied to smart agriculture: Analysis of a chatbot system design. In Proceedings of SAI Intelligent Systems Conference (pp. 1071–1088).
Downloads
Published
Issue
Section
License
Copyright (c) 2026 B. Jagadhesan, S. Devika, B. Sandeep Adavi, A. Naveen Kumar, S. Santhiya, C. Vaishali, Rajeshwari, Birendra Kumar Padhan

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