Optimized ResNet-18 Model for Ripeness Classification of Javanese Long Pepper (Piper retrofractum) Using Reflectance and Fluorescence Imaging - Sandra; Damayanti, Retno; Sa'diyah, Mitha; Nainggolan, Rut Juniar (Universitas Brawijaya, 2026)
Javanese long pepper (Piper retrofractum Vahl.) is a high-valuable horticultural commodity crop whose quality and bioactive composition depend strongly on fruit ripeness. Conventional visual assessment is often subjective and inconsistent, emphasizing the need for an objective and non-destructive alternative. This study aimed to develop an automated classification system for determining ripeness stages (green, orange, and red) using reflectance (visible light) and fluorescence (ultraviolet) imaging analyzed through a fine-tuned ResNet-18 deep learning model. A total of 300 images per modality were captured under controlled studio conditions and processed through standardized background removal, size normalization, and data augmentation. Color feature analysis across RGB, CIELAB, and HSV color spaces revealed that reflectance imaging effectively captured pigment transitions associated with chlorophyll degradation and carotenoid accumulation, with the a* channel serving as the most discriminative single-channel indicator. Fluorescence imaging provided complementary physiological information through chlorophyll emission dynamics, exhibiting greater inter-class overlap particularly between green and orange stages. The ResNet-18 model was evaluated using three optimizers (Adam, SGDM, and RMSprop) based on accuracy, precision, recall, Macro F1-score, and loss. For reflectance imaging, SGDM and RMSprop both achieved perfect test classification (accuracy = 100%, Macro F1 = 1.000), while fluorescence-based models achieved up to 97.78% accuracy. Training curve analysis confirmed stable convergence without overfitting across all combinations. Confusion matrix analysis showed that misclassifications were confined to adjacent ripeness stage boundaries, and Grad-CAM visualization confirmed physiologically consistent spatial attention patterns in the best-performing models. The proposed imaging and modeling pipeline offered a reproducible framework for postharvest quality evaluation and standardization of Piper retrofractum and similar horticultural commodities.