Loading...
Mr. K. Manikandan is an Assistant Professor in the Department of Plant Pathology, School of Agricultural Sciences, Dhanalakshmi Srinivasan University, Trichy, Tamil Nadu. His contribution focuses on intelligent irrigation and machine-learning-based crop yield prediction.
Agriculture encounters escalating difficulties associated with water shortages, climatic fluctuations, soil deterioration, rising population, and the necessity to enhance crop yields. Traditional irrigation methods frequently distribute water equally, disregarding real-time fluctuations in soil conditions, climatic factors, crop needs, and developmental phases. Accurate assessment of crop yields is crucial for agricultural management, food distribution strategies, and sustainable farming practices. Machine Learning (ML) offers sophisticated techniques for examining agricultural data and producing forecasts that can enhance irrigation management and crop yield projections. This section elucidates the essential principles, techniques, and utilisations of machine learning in the context of smart irrigation and crop yield forecasting. It examines the collection of agricultural data using soil sensors, meteorological stations, Internet of Things devices, satellite imaging, and historical farming records.Machine learning techniques including Decision Trees, Random Forest, Support Vector Machines, k-Nearest Neighbours, XGBoost, Artificial Neural Networks, Long Short-Term Memory networks, and hybrid models are explored. The section additionally introduces intelligent irrigation frameworks, soil moisture forecasting, water demand assessment, crop yield prediction, feature selection, performance analysis, and decision-making support systems.