A Robust and Explainable Approach to Crop Recommendation Using a Balanced Multi-Crop Agronomic Dataset

Crop recommendation systems play a critical role in supporting sustainable agricultural decision making under increasing climate variability. Modern machine learning approaches offer high predictive accuracy, yet their adoption in real-world agri-tech systems depends equally on robustness to environmental change and transparency of decision logic. Using a balanced multi-crop agronomic dataset, this study evaluates classical machine learning models, ensemble methods, and inherently interpretable rule-based learners under two evaluation settings: standard k-fold cross-validation and a rainfall-quartile protocol that simulates shifts in precipitation regimes. The results show that high accuracy under random data splits can substantially overestimate real-world performance when rainfall patterns change. To address this gap, we analyse the accuracy–explainability trade-off by comparing black-box ensembles with interpretable rule-based models. Feature attribution analysis based on SHAP further confirms that rainfall, humidity, and soil potassium are the most influential drivers of crop suitability. The findings provide a data-driven and explainable framework for developing climate-resilient crop recommendation systems that support environmental sustainability, resource-efficient farming, and informed decision making in precision agriculture.