Non-Destructive Classification of Apple Watercore Severity Levels Using Near-Infrared Hyperspectral Imaging

Abstract

Apple watercore is an internal physiological disorder that affects fruit quality and storage stability. This study developed a non-destructive approach for classifying watercore severity in 737 Aksu ‘Fuji’ apples using near-infrared hyperspectral imaging (930–1720 nm). Watercore severity was quantified using the watercore severity index (WSI) derived from Fiji-based segmentation of equatorial cross-sectional images, and samples were classified into sound, slight, moderate, and severe classes according to published criteria. After spectral preprocessing, support vector machine (SVM), random forest (RF), baseline one-dimensional convolutional neural network (1D-CNN), and attention-enhanced 1D-CNN-CBAM-SE models were evaluated using five repeated stratified holdout experiments. SVM and 1D-CNN-CBAM-SE achieved comparable performance, with SVM obtaining an accuracy of 98.74% and a Macro-F1 score of 98.86%, and 1D-CNN-CBAM-SE achieving the same accuracy and a Macro-F1 score of 98.85%. Ablation experiments showed that attention modules improved the baseline 1D-CNN, with CBAM providing the major performance gain and the addition of SE further reducing performance variability across repeated experiments. Misclassifications were largely confined to adjacent severity classes. These results support the feasibility of NIR-HSI combined with spectral classification models for non-destructive watercore severity grading.