The Role of Hyperparameter Tuning and Feature Engineering in Enhancing Machine Learning Models for Crop Disease Analysis

In this study, we provide a novel approach to teaching machine learning models to analyze farm illnesses. This strategy uses feature engineering and hyperparameter optimization. They are recursive feature elimination, random forest, and genetic algorithm optimization. Random Forest (RF, GAO, RFE). The recommended technique uses three algorithms to create it. Random Forest, an ensemble learning approach, combines decision tree forecasts to improve accuracy and usefulness. Each decision tree makes its own forecasts. Genetic algorithm optimization uses flexible hyperparameter adjustments to optimize the model. Natural selection served as the inspiration for this strategy. Iterative feature elimination improves the model. This procedure selects key issue aspects one by one. This work aims to enhance machine learning models for faster and better crop disease detection. These approaches will enable this.