Predicting De-Handing Point in Bananas Using Crown Morphology and Interpretable Machine Learning

Banana de-handing is a critical yet labor-intensive step in postharvest processing, with current manual methods resulting in high costs and occupational risks. This study addresses the automation of de-handing point localization by integrating high-resolution 3D scanning and morphometric analysis of banana crowns with machine learning techniques. A total of 210 crown samples were analyzed to extract key morphological features, including inner arc length (Li), inner arc radius (Ri), outer arc radius (Ro), and the distance between inner and outer arcs (Doi), among others. Four machine learning algorithms, namely, Multi-Layer Perceptron (MLP), Gradient Boosted Decision Trees (GBDT), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), were developed to predict the target radius (Rt) and target distance (Dti) of the de-handing point. The RF models achieved the optimal predictive performance on the testing set, with the following results: for Rt, R2 = 0.95, MAE = 1.50, and RMSE = 1.94; for Dti, R2 = 0.91, MAE = 1.33, and RMSE = 1.66. A Shapley Additive Explanations (SHAP) analysis revealed that Li, Ri, and Ro were the most influential features for Rt, while Doi was the most important for Dti. Notably, feature threshold effects were observed, with limited gains in prediction accuracy beyond specific morphological values. These results provide a quantitative foundation for vision-guided automated de-handing systems, advancing intelligent and efficient banana postharvest management.