Background: Crop recommendation is one of the most critical challenges in precision agriculture, requiring accurate predictions and rigorous evaluation for reliable decisions. However, previous research has employed ad hoc model selection, lack of statistical validation and limited interpretability, thereby limiting reproducibility and scientific credibility. 
 
Methods: A statistically rigorous multi-classifier benchmarking framework for crop recommendation is developed. A publicly available dataset was refined to 33,000 agronomically relevant samples from 57,000, covering 33 crop classes and seven features. A novel K-fold selection criterion optimising predictive accuracy, stability and diversity selected K=7. Friedman testing (&chi;2=59.23, p=1.89&times;10-9) with Nemenyi post-hoc analysis confirmed statistical significance of inter-model differences. 
 
Result: Random Forest was selected as the top-ranked method through a transparent multi-criteria framework. Optuna TPE (50 trials) yielded CV F1=96.63%, Test F1=97.06%, AUC=0.9996 and generalisation gap=0.47%. Humidity and rainfall are consistently dominant predictors in both MDI and SHAP, while potassium has elevated SHAP importance, reinforcing its agronomic relevance. The frame work enhances reliability, transparency and interpretability for data-driven agricultural decision-making.

A Statistically Rigorous Multi-classifier Benchmark for Crop Recommendation: K-fold Selection Criterion, Friedman-nemenyi Testing and Optuna TPE Optimisation