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 (L_i), inner arc radius (R_i), outer arc radius (R_o), and the distance between inner and outer arcs (D_oi), 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 (R_t) and target distance (D_ti) of the de-handing point. The RF models achieved the optimal predictive performance on the testing set, with the following results: for R_t, R² = 0.95, MAE = 1.50, and RMSE = 1.94; for D_ti, R² = 0.91, MAE = 1.33, and RMSE = 1.66. A Shapley Additive Explanations (SHAP) analysis revealed that L_i, R_i, and R_o were the most influential features for R_t, while D_oi was the most important for D_ti. 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.