Description of Invention
This study delves into how different machine learning techniques can be used with up, to date pest data to create dynamic pest management systems. We examine machine learning algorithms like Random Forests and Neural Networks in addition to Support Vector Machines (SVM) for forecasting pest infestations and suggesting methods for control purposes. Our strategy involves utilizing up to date information from weather sensors well as soil moisture meters along with pest detection systems for training and validating these models. We assess the effectiveness of these models by considering metrics such, as precision accuracy and Predictive score. The outcomes reveal that Neural Networks exhibit the level of precision in predicting pest outbreaks followed by Random Forest and SVM.