The integration of Artificial Intelligence (AI) in agriculture is revolutionizing traditional farming practices, driving efficiency, sustainability, and productivity. This chapter provides a comprehensive overview of AI applications in key agricultural processes, starting with an introduction to the transformative potential of AI in modern farming. The discussion then delves into soil management, highlighting how AI-driven tools optimize soil health assessment and nutrient management. In weed and disease control, AI-powered systems enable precise detection and targeted interventions, minimize the use of chemicals and reduce crop damage. Irrigation practices have also seen significant advancements through AI, with smart irrigation systems that monitor and manage water usage, enhancing water conservation and crop yield. Additionally, the chapter explores AI's role in crop mapping and monitoring, where satellite imagery and machine learning algorithms offer real-time insights into crop health, growth patterns, and yield predictions.
Drones, Smart agriculture, Robotics, Precision Farming, Software
Andresen, S.L., 2002. John McCarthy: father of AI. IEEE Intelligent Systems, 17 (5), 84–85.
Åstrand, B., Baerveldt, A.-J., 2002. Auton. Robot. 13 (1), 21–35.
Awais, M., Naqvi, S.M.Z.A., Zhang, H., Li, L., Zhang, W., Awwad, F.A., Ismail, E.A.A., Khan, M.I., Raghavan, V., and Hu, J., 2023. AI and machine learning for soil analysis: an assessment of sustainable agricultural practices. Bioresources and Bioprocessing, 10 (1), 90.
DeOca, A.M., Arreola, L., Flores, A., Sanchez, J., Flores, G., 2018. Low-cost multispectral im aging system for crop monitoring. 2018 International Conference on Unmanned Air craft Systems (ICUAS)
Elbeltagi, A., Kushwaha, N. L., Srivastava, A., & Zoof, A. T. (2022). Artificial intelligent-based water and soil management. In Deep learning for sustainable agriculture (pp. 129-142). Academic Press.
Filgueiras, R., Almeida, T.S., Mantovani, E.C., Dias, S.H.B., Fernandes-Filho, E.I., da Cunha, F.F., and Venancio, L.P., 2020. Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data. Agricultural Water Management, 241, 106346.
Gómez-Candón, D.; Virlet, N.; Labbé, S.; Jolivot, A.; Regnard, J.L, 2016. Field phenotyping of water stress at tree scale by UAV-sensed imagery: New insights for thermal acquisition and calibration. Precision Agriculture, 17, 786–800.
Javaid, M., Haleem, A., Khan, I.H., and Suman, R., 2023. Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem, 2 (1), 15–30.
Jha, K., Doshi, A., Patel, P. and Shah, M., 2019. A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2, pp.1-12.
Jordan, M.I. and Mitchell, T.M., 2015. Machine learning: Trends, perspectives, and prospects. Science, 349 (6245), 255–260.
Jose, A., Nandagopalan, S., and Akana, C.M.V.S., 2021. Artificial Intelligence techniques for agriculture revolution: a survey. Annals of the Romanian Society for Cell Biology, 2580–2597.
Misra, N.N., Dixit, Y., Al-Mallahi, A., Bhullar, M.S., Upadhyay, R., and Martynenko, A., 2022. IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry. IEEE Internet of Things Journal, 9 (9), 6305–6324.
Nakai, S., Yamada, Y., 2014. Development of a Weed Suppression Robot for Rice Cultivation: Weed Suppression and Posture Control. International Journal of Electrical, Computer, Electronics and Communication Engineering, 8 (12), 1658–1662.
Pastén-Zapata, E., Ledesma-Ruiz, R., Harter, T., Ramírez, A.I., and Mahlknecht, J., 2014. Assessment of sources and fate of nitrate in shallow groundwater of an agricultural area by using a multi-tracer approach. Science of The Total Environment, 470–471, 855–864.
Patil, R.R., Kumar, S., and Rani, R., 2022. Comparison of Artificial Intelligence Algorithms in Plant Disease Prediction. Revue d’Intelligence Artificielle, 36 (2).
Peruzzi, A.; Martelloni, L.; Frasconi, C.; Fontanelli, M.; Pirchio, M.; Raffaelli, M, 2017. Machines for non-chemical intra-row weed control: A review. J. Agric. Eng. 48, 57–70.
Prabha, K., 2021. Disease sniffing robots to apps fixing plant diseases: applications of artificial intelligence in plant pathology—a mini review. Indian Phytopathology, 74 (1), 13–20.
Savary, S., Willocquet, L., Pethybridge, S.J., Esker, P., McRoberts, N., and Nelson, A., 2019. The global burden of pathogens and pests on major food crops. Nature Ecology & Evolution, 3 (3), 430–439.
Singh, P. and Kaur, A., 2022. A systematic review of artificial intelligence in agriculture. In: Deep Learning for Sustainable Agriculture. Elsevier, 57–80.
Singha, C., & Swain, K.C. (2021). Rice and Potato Yield Prediction Using Artificial Intelligence Techniques. Studies in Big Data.
Strange, R.N. and Scott, P.R., 2005. Plant Disease: A Threat to Global Food Security. Annual Review of Phytopathology, 43 (1), 83–116.
Su, W.H., 2020. Crop plant signaling for real-time plant identification in smart farm: A systematic review and new concept in artificial intelligence for automated weed control. Artificial Intelligence in Agriculture, 4, pp.262-271.
Su, W.-H., Fennimore, S.A., Slaughter, D.C., 2019. Fluorescence imaging for rapid monitoring of translocation behaviour of systemic markers in snap beans for automated crop/weed discrimination. Biosyst. Eng. 186, 156–167
Verma, B.L. and Kumawat, R.C., 2020. Constraints being Faced by the Farmers in the Production of Major Field Crops in the State of Rajasthan. International Journal of Current Microbiology and Applied Sciences, 9 (6), 1763–1773.
Wang, H., Garg, A., Ping, Y., Sreedeep, S., and Chen, R., 2022. Effects of Biochar Derived from Coconut Shell on Soil Hydraulic Properties under Salt Stress in Roadside Bioretention. Waste and Biomass Valorization.
Yanagi, M., 2024. Wheat: Challenges and opportunities in combating climate change. Resources Data Journal, 3, 21–25.
Zhao, H., Xiao, Q., Miao, Y., Wang, Z., and Wang, Q., 2020. Sources and transformations of nitrate constrained by nitrate isotopes and Bayesian model in karst surface water, Guilin, Southwest China. Environmental Science and Pollution Research, 27 (17), 21299–21310
