Machine Learning Applications in Predictive Pest Modeling for Developing Pest-Resistant Crop Varieties

Abstract

This chapter explores the use of machine learning tools in developing models to predict pest attacks and develop resistant crops using large data sets for reliable predictions. Decision trees and neural networks aid in pest prediction by creating resistant crop varieties, reducing chemical usage, and enhancing farming sustainability. The study examines issues in machine learning-driven pest prediction, including data quality, ease of understanding, and expansion, and proposes methods to enhance learning from data and combine multiple methods. This section discusses the use of machine learning tools in developing models to predict pest invasions and develop crops that can resist pests in farming. The study addresses issues like data quality, interpretation ease, and growth potential, suggesting methods to improve data learning and merge learning styles. Future research should refine these models and develop pest-resistant crop varieties.