Machine Learning and SHAP for Morphological Feature Identification of Longjing Tea Straightness

The sensory evaluation of Longjing tea quality relies predominantly on subjective human judgment. Straightness is a critical sensory descriptor in the morphological quality assessment of Longjing tea, which lacks objective and quantifiable criteria. To objectively quantify this descriptor from a morphological perspective, this study developed an interpretable machine learning framework that integrates computer vision and SHAP analysis to digitally characterize the sensory attribute of straightness. Nine morphological features were extracted from Longjing tea samples collected across major production regions, including six contour-based and three skeleton-based descriptors. Three classification models, namely random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM), were constructed and compared based on morphological features. SHAP analysis was employed to interpret model decisions, identifying three core morphological determinants, length ratio (lr), standard deviation (sd) and rectangularity (R), which consistently ranked highest in both univariate effect sizes and cross-model feature importance, with Cliff’s δ of 0.93, 0.81 and −0.78, respectively. By establishing a quantitative link between morphological features and sensory terminology, this study offers an interpretable tool for automated tea quality evaluation, with broader implications for digital standardization in quality evaluation of agricultural products.