Machine Learning Algorithms for Predictive Pest Modeling in Agricultural Crops

Abstract

Food security and maximum yield depend on accurate pest prediction and crop management. An in-depth analysis of this cutting-edge area is the goal of this book chapter, which will explore predictive pest modeling using machine learning (ML) algorithms. The introduction establishes the section by stressing the significance of ML in transforming crop pest management and the value of predictive pest modeling. Furthermore, it will delve into various ML techniques designed for pest modeling. Differentiating between supervised, unsupervised, and semi-supervised learning techniques, it will outline a range of ML methods. Moreover, to help practitioners make an educated decision, it will also focus on the criteria for algorithm selection in pest prediction. It concludes with a detailed overview of ML algorithms' revolutionary potential in agricultural operations and their importance in predictive pest modeling.