Artificial Intelligence and Application: Application of Machine Learning, Deep Learning, Internet of Things and Digital Farming
Keywords:
Artificial Intelligence, Climate Resilience, Digital farming, GIS, , GPS, IoTAbstract
Currently, agriculture is under pressure from a rising population, decreasing land availability, changes in climate, and increasing costs of inputs. Artificial Intelligence (AI) and other related technologies are becoming more and more useful in helping farmers to make better decisions and to use their resources more efficiently. The article looks at the concepts and the applications of AI, Machine Learning (ML), Deep Learning (DL) and the Internet of Things (IoT). It describes how the combination of these technologies forms the basis of Digital Farming. ML allows for the prediction of yields, the classification of soil, and the recommendation of fertilizer and irrigation schedules. Meanwhile, DL, which makes use of Convolutional Neural Networks, enables precise detection of pests, diseases and weeds based on images. IoT links together field sensors, weather stations and farming machinery to create instant monitoring networks, and Digital Farming integrates these tools with remote sensing, GPS/GIS and mobile applications in order to support data-driven, site-specific crop management. The article also examines the advantages, a practical workflow, the challenges and the future prospects, referring to the agricultural situation in India and to the initiatives carried out by the Indian Council of Agricultural Research (ICAR) and the Government of India.
References
Ahmad, A., Liew, A. X. W., Venturini, F., Kalogeras, A., Candiani, A., Di Benedetto, G., Ajibola, S., Cartujo, P., Romero, P., Lykoudi, A., De Grandis, M. M., Xouris, C., Lo Bianco, R., Doddy, I., Elegbede, I., D’Urso Labate, G. F., García del Moral, L. F., & Martos, V. (2024). AI can empower agriculture for global food security: Challenges and prospects in developing nations. Frontiers in Artificial Intelligence, 7, 1328530. https://doi.org/10.3389/frai.2024.1328530
Aijaz, N., Lan, H., Raza, T., Yaqub, M., Iqbal, R., & Pathan, M. S. (2025). Artificial intelligence in agriculture: Advancing crop productivity and sustainability. Journal of Agriculture and Food Research, 20, 101762. https://doi.org/10.1016/j.jafr.2025.101762
Albahar, M. (2023). A survey on deep learning and its impact on agriculture: Challenges and opportunities. Agriculture, 13(3), 540. https://doi.org/10.3390/agriculture13030540
Attri, I., Awasthi, L. K., & Sharma, T. P. (2024). Machine learning in agriculture: A review of crop management applications. Multimedia Tools and Applications, 83, 12875–12915. https://doi.org/10.1007/s11042-023-16105-2
Bhattarai, R., Koch, J., Jentner, W., Habib, M., Wimberly, M. C., & Ebert, D. (2026). Scale, trust, and the digital divide: A systematic review of AI and ML for agricultural applications. Frontiers in Artificial Intelligence, 9, 1798896. https://doi.org/10.3389/frai.2026.1798896
Dara, R., Hazrati Fard, S. M., & Kaur, J. (2022). Recommendations for ethical and responsible use of artificial intelligence in digital agriculture. Frontiers in Artificial Intelligence, 5, 884192. https://doi.org/10.3389/frai.2022.884192
Dash, R., Jena, C., Pramanik, K., & Mohapatra, P. P. (2021). Vegetable grafting: A noble way to enhance production and quality. The Pharma Innovation Journal, 10(8), 1580–1584.
Dhal, S., Wyatt, B. M., Mahanta, S., Bhattarai, N., Sharma, S., Rout, T., Saud, P., & Acharya, B. S. (2024). Internet of Things (IoT) in digital agriculture: An overview. Agronomy Journal, 116(3), 1144–1163. https://doi.org/10.1002/agj2.21385
Dhanaraju, M., Chenniappan, P., Ramalingam, K., Pazhanivelan, S., & Kaliaperumal, R. (2022). Smart farming: Internet of Things (IoT)-based sustainable agriculture. Agriculture, 12(10), 1745. https://doi.org/10.3390/agriculture12101745
Food and Agriculture Organization of the United Nations. (2025). Digital agriculture and AI innovation roadmap for the global agrifood systems transformation. FAO. https://doi.org/10.4060/cd5956en
Gakhar, S., Koo, J., Patnaik, G. P., & Singh, R. K. R. (2026). Bridging agronomic science and context specific farm-level advisory through generative AI for rice systems in India. Frontiers in Artificial Intelligence, 9, 1808028. https://doi.org/10.3389/frai.2026.1808028
Husain, A., & Rehmat, A. (2025). Digital agriculture and information and communication technology for ensuring sustainable development in India: A review. Asian Journal of Agricultural Extension, Economics & Sociology, 43(6), 249–258. https://doi.org/10.9734/ajaees/2025/v43i62780
Jabed, M. A., & Murad, M. A. A. (2024). Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability. Heliyon, 10(24), e40836. https://doi.org/10.1016/j.heliyon.2024.e40836
Khanna, M., Atallah, S. S., Heckelei, T., Wu, L., & Storm, H. (2024). Economics of the adoption of artificial intelligence-based digital technologies in agriculture. Annual Review of Resource Economics, 16, 41–61. https://doi.org/10.1146/annurev-resource-101623-092515
Manzoor, F., Wei, L., Siraj, M., Lu, X., & Qiyang, G. (2025). Digital agriculture technology adoption in low and middle-income countries—a review of contemporary literature. Frontiers in Sustainable Food Systems, 9, 1621851. https://doi.org/10.3389/fsufs.2025.1621851
Pradhan, J., Pramanik, K., Jaiswal, A., Kumari, G., Prasad, K., Jena, C., & Srivastava, A. K. (2024). Biosynthesis of secondary metabolites in aromatic and medicinal plants in response to abiotic stresses: A review. Journal of Experimental Biology and Agricultural Sciences, 12(3), 318–334. https://doi.org/10.18006/2024.12(3).318.334
Press Information Bureau, Government of India. (2026, February 14). Artificial Intelligence (AI) transforming Indian agriculture. Ministry of Agriculture & Farmers Welfare. https://www.pib.gov.in/PressReleasePage.aspx?PRID=2227914
Shaikh, F. K., Karim, S., Zeadally, S., & Nebhen, J. (2022). Recent trends in Internet-of-Things-enabled sensor technologies for smart agriculture. IEEE Internet of Things Journal, 9(23), 23583–23598. https://doi.org/10.1109/JIOT.2022.3210154
Shaikh, T. A., Rasool, T., & Lone, F. R. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198, 107119. https://doi.org/10.1016/j.compag.2022.107119
Sudharani, Y., Mohapatra, P. P., Pramanik, K., & Maitra, S. (2018). Effect of different phosphorus levels on growth and yield of cowpea (Vigna unguiculata L.) genotypes. International Journal of Management, Technology and Engineering, 8(11), 2876–2881.
