Machine Learning for Precision Agriculture and Crop Yield Optimization

The swift advancement of machine learning (ML) has altered several industries, including agriculture, by providing innovative ways of addressing complex challenges related to modern farming. This chapter discusses the use of ML in precision agriculture, emphasizing its capacity to maximize crop output and improve agricultural practices. It studies the use of supervised, unsupervised, reinforcement, and deep learning methodologies to evaluate extensive datasets derived from remote sensing technologies, soil sensors, climate data, and agricultural equipment. Principal applications include predictive modeling for agricultural yield estimation, pest and disease identification, soil health assessment, irrigation optimization, and precision fertilization. The chapter also examines the problems and limits related to the implementation of machine learning in agriculture, including data quality and farmer acceptance.