Crop Recommendation System Using Machine Learning: A Comparative Study

Abstract

Agriculture, as a fundamental aspect of human existence, faces challenges in crop selection, impacting resource allocation and productivity. This project addresses these challenges by proposing a stable system employing a soft voting classifier ensemble method. The ensemble comprises Naive Bayes, Support Vector Machine (SVM), Decision Tree, and Random Forest classifiers, offering personalized crop recommendations. Feasibility analysis encompasses technical, operational, economic, and scheduling aspects, ensuring practicality and efficacy. Development follows an incremental model, emphasizing continuous enhancement through feedback. Results indicate accuracies for individual classifiers (’Decision Tree’: 98.38%, ’Random Forest’: 98.90%, ’Naive Bayes’: 98.14%, ’SVM’: 98.50%), with an ensemble accuracy of 98.99%. Cross-validation confirms robustness. Evaluation metrics such as recall, precision, and F1 score demonstrate that the soft voting ensemble outperforms individual classifiers, highlighting its effectiveness in optimizing crop selection processes in agriculture and facilitating improved resource management and productivity.