Advanced AI-Driven Crop Recommendation System for Maximizing Agricultural Efficiency and Sustainability

Abstract

This study introduces a machine learning-based crop recommendation system designed to assist farmers in selecting suitable crops based on soil and environmental parameters. The system utilizes historical and real-time data, including temperature, humidity, pH level, and rainfall, to generate personalized crop suggestions. After performing data cleaning, normalization, and feature encoding, multiple machine learning algorithms were evaluated to determine the best-performing model. The Random Forest Classifier demonstrated superior accuracy (93.2%) and robustness in handling complex, high-dimensional agricultural data, outperforming other models such as Support Vector Machine (SVM), Decision Tree, and Logistic Regression.

The model's predictive performance was assessed using standard classification metrics such as accuracy, precision, recall, and the F1-score. Grid Search was applied to fine-tune hyperparameters, while K-fold cross-validation ensured model generalizability. The system also integrates real-time environmental updates and market-aware decision-making through IoT inputs and feedback loops. The goal is to empower farmers with a user-friendly, data-driven platform that promotes sustainable agriculture and maximizes crop productivity.