Abstract: The rapid growth of global food demand alongside increasing climate variability has rendered traditional crop selection methods inadequate. This paper presents an AI-based crop recommendation system that applies supervised machine learning to predict the optimal crop for cultivation based on soil composition and environmental parameters. Using the Crop Recommendation Dataset (2,200 samples, 22 crop classes, 7 agronomic features), five classification algorithms were evaluated: Naive Bayes, Decision Tree, Support Vector Machine, Random Forest, and Gradient Boosting. Random Forest achieved the highest accuracy of 96.8%, outperforming all baselines. The system provides a scalable, data-driven decision support tool for precision agriculture, addressing soil nutrient imbalances and climate variability challenges faced by farmers.

Keywords: Crop Recommendation, Machine Learning, Random Forest, Soil Parameters, Precision Agriculture, Classification, Decision Support System

Downloads: | DOI: 10.17148/IARJSET.2026.13325