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All Journal Jurnal Keteknikan Pertanian Tropis dan Biosistem Jurnal Pengabdian UntukMu NegeRI Journal of Functional Food and Nutraceutical Informasi dan Promosi Kesehatan Journal of Scientific Research, Education, and Technology MANKEU (JURNAL MANAJEMEN KEUANGAN) Tadulako Science and Technology Journal JURNAL SATYA MANDIRI MANAJEMEN DAN BISNIS Jurnal Cakrawala Akademika Journal of Golden Generation Abdimas
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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
Jurnal Keteknikan Pertanian Tropis dan Biosistem Vol. 14 No. 1 (2026): April 2026
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/

Abstract

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.
Co-Authors alexander Alifia Ramadhani Arif Firmansyah Badaruddin Defi Eka Kartika Herlina Intan Dwi Lestari Jalil, Muhammad Kartika, Defi Eka Khadijah Darwin Khairunnisa Lismita Maharani Marpaung, Abdullah Muzi Martha Yolanda Muh. Revan Ashari Muin , M. R. Nainggolan, Rut Juniar Noviyani, Dwi Peny Cahaya Azwari Retno Damayanti Rosyadi, Imran Rusydi. H , M. Sa'diyah, Mitha Surya, Vincent Satya Taufik kurahman Th. Musa , Moh. Dahlan Veralia WAHYUNI winda suriyati Yanto, M.F.W Yolanda , Martha Yolanda, Martha Yuli Wardani Putri