A Multi-Model AI Framework for Optimized Crop Prediction and Yield Estimation

Precision agriculture has become an essential practice for optimizing crop yield and resource utilization. This research integrates classification and regression models to enhance crop prediction and yield estimation. The study employs a Random Forest Classifier for crop recommendation and utilizes regression models such as Linear Regression, Decision Tree Regressor, and K-Neighbors Regressor for yield estimation. Feature Importance Analysis is also applied to identify key factors influencing crop growth. The proposed hybrid approach aims to improve prediction accuracy, scalability, and practical applicability, providing a comprehensive decision-support system for farmers.