Predicting Crop Yields Using Agricultural Environment Characteristics with Advanced Feature Selection and Classifiers

This study investigates the use of machine learning classifiers and sophisticated feature selection approaches for crop prediction based on environmental factors. The study builds a predictive model using information on crop history, weather patterns, and soil type. To lower dimensionality and increase model accuracy, feature selection techniques like Principal Component Analysis (PCA) and Random Forest are used. Algorithms like Decision Trees, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) are used for categorization. Real-time decision-making and on-site predictions in agricultural fields are made possible by the framework, which can be installed on hardware such as Raspberry Pi and Arduino modules. The model shows a high degree of accuracy, giving farmers a useful tool to maximize crop productivity.