Loading...
Dr. Ritu Nagila is an Assistant Professor in the Department of Computer Science and Engineering at the School of Computer Science and Applications, IFTM University, Moradabad, India. Her research interests include Artificial Intelligence, Machine Learning, precision agriculture, data analytics, Internet of Things, and intelligent computing. She actively contributes to research on AI-enabled agricultural technologies and sustainable smart farming solutions.
Agriculture is seeing a profound evolution due to the integration of Artificial Intelligence (AI), Internet of Things (IoT), cloud computing, remote sensing, and precision farming technology. The escalating worldwide need for food, fluctuations in climate, diminishing soil fertility, and restricted natural resources necessitate astute agricultural methods that enhance output while maintaining sustainability. Crop yield forecasting has emerged as a critical use of artificial intelligence, allowing farmers and agricultural entities to anticipate output, enhance resource efficiency, minimise operating expenses, and facilitate informed decision-making. IoT devices incessantly assess environmental and agricultural conditions by gathering instantaneous data on soil moisture, temperature, humidity, precipitation, nutrient concentrations, and crop vitality. Machine Learning (ML), Deep Learning (DL), satellite imaging, unmanned aerial vehicles (UAVs), and Explainable Artificial Intelligence (XAI) enhance predictive precision and agricultural decision-making assistance. This chapter elucidates the principles, framework, artificial intelligence methodologies, applications, obstacles, and prospective research avenues pertaining to AI-driven crop yield forecasting and precision agriculture utilising IoT.