December 3, 2025Open Access

Data-Driven Crop Recommendation Using Machine Learning: A Comparative Analysis

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Analysis reveals Random Forest outperforms other algorithms in crop recommendation, suggesting improved precision agriculture guidelines.

Random Forest achieved 99.32% accuracy in crop recommendation, outperforming other models significantly.
Feature importance analysis highlighted rainfall and humidity as critical agronomic factors.
Supervised learning algorithms evaluated include Random Forest, Decision Tree, and Support Vector Machine, with optimized performance.
Findings support algorithm selection for intelligent crop recommendation systems in precision agriculture.

Simona et al. (2025) studied this question.

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