Comparison of ResNet CNN and Optimized Vision Transformer Model for Classification of Dried Moringa Leaf Quality
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The quality classification of dried Moringa leaves is an essential task in the agricultural and food processing industries due to its direct impact on product value and consumer acceptance. This study aims to compare the performance of a Convolutional Neural Network (CNN) based on ResNet architecture with an optimized Vision Transformer (ViT) model for automated classification of dried Moringa leaf quality. The methodology involved preprocessing and normalization of image data, followed by training and evaluation of both models under identical experimental settings. The ResNet CNN achieved an overall accuracy of 68%, showing strong performance in certain classes such as “A” (precision 0.78, recall 0.90) and “F” (precision 0.80, recall 1.00), but poor recognition of class “D.” Conversely, the optimized Vision Transformer model attained an accuracy of 60%, demonstrating robust classification for classes “C” (f1-score 0.77) and “D” (f1-score 0.79), though it struggled with class “E.” The findings indicate that while ResNet CNN yields higher overall accuracy, the Vision Transformer shows potential in handling complex visual variations with optimization. This study contributes to the development of AI-based agricultural quality assessment systems by providing comparative insights into deep learning architectures for image-based classification.
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Santiyuda, K., Febyanti, P., Wibawa, G. P., Welson, S., & Sutrisna, I. M. (2025). Comparison of ResNet CNN and Optimized Vision Transformer Model for Classification of Dried Moringa Leaf Quality. Jurnal Sistem Informasi Dan Komputer Terapan Indonesia (JSIKTI), 7(4), 118-127. https://doi.org/10.33173/jsikti.215
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
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[4] L. P. Wulandari and A. Hapsari, “The EFFECT OF Moringa Oleifera LEAF EXTRACT ON THECA CELL IN POLYCYSTIC OVARY SYNDROME MODEL WITH INSULIN RESISTANCE,” J. Kedokt. Hewan - Indones. J. Vet. Sci., vol. 14, no. 3, 2020, doi: 10.21157/j.ked.hewan.v14i3.16919.
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[6] T. O. Olabanji, O. M. Ojo, C. G. Williams, and A. S. Adewuyi, “Assessment of Moringa Oleifera Seeds as a Natural Coagulant in Treating Low Turbid Water,” Fuoye J. Eng. Technol., vol. 6, no. 4, 2021, doi: 10.46792/fuoyejet.v6i4.702.
[7] A. C. P. Ferraz, I. F. S. dos Santos, A. M. ́és L. Silva, R. M. Barros, M. V. L. Martins, and A. L. Junho, “Wastewater Treatment From Potato Processing Industry Using Moringa Oleifera-Based Coagulant,” Rev. Ibero-Americana Ciências Ambient., vol. 12, no. 7, pp. 211–224, 2021, doi: 10.6008/cbpc2179-6858.2021.007.0020.
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[26] G. M. S. Himel, M. M. Islam, K. A. Al-Aff, S. I. Karim, and M. K. U. Sikder, “Skin
Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-Based Noninvasive Digital System,” Int. J. Biomed. Imaging, vol. 2024, pp. 1–18, 2024, doi: 10.1155/2024/3022192.
[27] J. Maurício, I. Domingues, and J. Bernardino, “Comparing Vision Transformers and Convolutional Neural Networks for Image Classification: A Literature Review,” Appl. Sci., vol. 13, no. 9, p. 5521, 2023, doi: 10.3390/app13095521.
[28] J. Selvaraj, F. Almutairi, S. M. Aslam, and U. Snekhalatha, “Binary and Multi-Class Classification of Colorectal Polyps Using CRP-ViT: A Comparative Study Between CNNs and QNNs,” Life, vol. 15, no. 7, p. 1124, 2025, doi: 10.3390/life15071124.
[29] M. A. Hasan et al., “Mulberry Leaf Disease Detection by CNN-ViT With XAI Integration,” PLoS One, vol. 20, no. 6, p. e0325188, 2025, doi: 10.1371/journal.pone.0325188.
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[32] S. Zafar, A. Muhammad, and D. Han, “Plant Disease Classification in the Wild Using Vision Transformers and Mixture of Experts,” Front. Plant Sci., vol. 16, 2025, doi: 10.3389/fpls.2025.1522985.
