December 3, 2025Open Access

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

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Comparative analysis supports higher precision agriculture with machine learning algorithms, suggesting optimal crop recommendation strategies.

Random Forest achieved the highest accuracy of 99.32%, outperforming other models.
The analysis compares Random Forest, Decision Tree, and Support Vector Machine implementations on crop data.
Hyperparameter optimization enabled fair comparisons among supervised learning algorithms in agricultural settings.
Findings highlight the importance of rainfall and humidity, pointing to precision agriculture advancements.

Simona et al. (2025) studied this question.

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Comparative analysis supports higher precision agriculture with machine learning algorithms, suggesting optimal crop recommendation strategies.