Analisis Komparasi Arsitektur Deep Learning untuk Klasifikasi Penyakit Daun Cabai
DOI:
https://doi.org/10.30865/jurikom.v13i1.9483Keywords:
Deep Learning, VGG16, ResNet50, InceptionV3, Chili Disease ClassificationAbstract
The productivity of chili (Capsicum annuum L.) in Indonesia faces significant challenges due to leaf diseases, which are estimated to reduce harvest yields by up to 35%. Conventional detection methods relying on visual observation often lack accuracy due to the high visual similarity between disease symptoms. This study focuses on a comparative evaluation of three leading Deep Learning architectures VGG16, ResNet50, and InceptionV3 in classifying six types of chili leaf diseases using a public dataset. The research implements a high-resolution image strategy (512 x 512 pixels) to maximize the extraction of disease texture features. The methodology employs a Transfer Learning approach with a standardized hyperparameter tuning scheme. Experimental results indicate that the use of high-resolution images significantly impacts model accuracy. The VGG16 architecture achieved the best performance with a testing accuracy of 99.83% and an F1-Score of 1.00, outperforming ResNet50 (99.75%) and InceptionV3 (84.00%). Confusion Matrix analysis demonstrates that VGG16 possesses superior stability in distinguishing disease classes with high visual similarity, such as Bacterial Spot and Cercospora. The study concludes that architectures preserving deep spatial information, such as VGG16, are more effective for high-resolution image-based plant disease diagnosis compared to more complex architectures that perform aggressive feature compression.
References
[1] FAO, “Global agriculture towards 2050,” FAO, 2012, [Online]. Available: https://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/HLEF2050_Global_Agriculture.pdf
[2] FAO, “Cooperation at all levels and funding ‘critical’ for plant health and food security,” Rome, Italy, 2025. [Online]. Available: https://www.fao.org/newsroom/detail/cooperation-at-all-levels-and-funding--critical--for-plant-health-and-food-security--fao-says/en
[3] J. Li, “Compound impact of COVID-19, economy and climate on the spatial distribution of global agriculture and food security,” Sci. Total Environ., vol. 876, p. 162796, 2023, [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC10039698/
[4] R. and S. Asia, “EL NIÑO IMPACT ON AGRICULTURE IN SOUTHEAST ASIA,” 2023. [Online]. Available: https://rassea.org/2023/10/03/el-nino-impact-on-agriculture-in-southeast-asia/
[5] “Begomovirus: Potensi ancaman pertanian hortikultura Lampung,” J. Pertan. Agros, vol. 45, no. 1, pp. 1–10, 2023, [Online]. Available: https://jpa.fp.unila.ac.id/index.php/jpa/article/view/31
[6] FAO, “The state of food and agriculture,” Rome, Italy, 2023. [Online]. Available: https://www.fao.org/documents/card/en/c/cc7724en
[7] “Changing food systems and infectious disease risks in low-income and middle-income countries,” Lancet Planet. Heal., vol. 5, no. 9, pp. e588–e597, 2021, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2542519622001164
[8] B. P. S. S. I. K. Regency, “Cabai Besar (Large Chili),” 2023. [Online]. Available: https://kebumenkab.bps.go.id/en/statistics-table/2/NzMjMg==/cabai-besar.html
[9] M. A. Khan, “Chili cultivars vulnerability: a multi-factorial examination of disease impact and yield reduction,” Front. Plant Sci., vol. 15, p. 1522985, 2024, [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC11488149/
[10] “Mitigation and Adaptation of Chili Farmers to Climate Change,” Int. J. Des. Nat. Ecodynamics, vol. 19, no. 4, pp. 567–574, 2024, [Online]. Available: https://www.iieta.org/journals/ijdne/paper/10.18280/ijdne.190432
[11] “Molecular Diversity of Pepper yellow leaf curl Indonesia virus,” J. Penelit. Tanam. Ind. dan Penyegar, vol. 10, no. 2, pp. 89–98, 2023, [Online]. Available: https://jurnal.ugm.ac.id/v3/jtbb/article/download/15670/5546/
[12] S. Savary, “Certification, good agricultural practice and smallholder farmers,” Glob. Food Sec., vol. 21, pp. 50–58, 2019, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0959378018306897
[13] “A Mobile Application for Plant Disease Detection,” in Global Media Publication International Conference on Science, 2023, pp. 120–130. [Online]. Available: https://journal.gmpionline.com/index.php/gmpics/article/view/173
[14] “Deep Learning Approaches for Plant Disease Diagnosis Systems: A Review,” J. Agric. Appl. Stat., vol. 1, no. 1, pp. 1–15, 2025, [Online]. Available: https://jaast.org/index.php/jaast/article/download/308/208
[15] “Machine learning techniques for plant disease detection,” J. Big Data, vol. 10, no. 1, pp. 1–25, 2023, [Online]. Available: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-023-00863-9
[16] K. . Z. Simonyan A., “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv, 2014, [Online]. Available: https://arxiv.org/pdf/1409.1556.pdf
[17] K. . Z. He X.; Ren, S.; Sun, J., “Deep Residual Learning for Image Recognition,” CVPR, 2016, [Online]. Available: https://doi.org/10.1109/CVPR.2016.90
[18] C. . V. Szegedy V.; Ioffe, S.; Shlens, J.; Wojna, Z., “Rethinking the Inception Architecture for Computer Vision,” CVPR, 2016, [Online]. Available: https://doi.org/10.1109/CVPR.2016.308
[19] M. Data, “Chili Plant Leaf Disease and Growth Stage Dataset from Bangladesh,” 2025. [Online]. Available: https://data.mendeley.com/datasets/w9mr3vf56s/1
[20] S. H. Kim, “Microclimates growing and watering volumes influences the physiological traits of chili pepper cultivars in combating abiotic stress,” PMC, 2025, [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC11794617/
[21] A. K. Das, “Assessing the climate adaptive potential of imported Chili in comparison with local cultivars through germination performance analysis,” PMC, 2024, [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC11177443/
[22] R. K. Sharma, “Strategies to develop climate-resilient chili peppers,” PMC, 2025, [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC12174194/
[23] A. Mumtaz, “Global range expansion history of pepper (Capsicum spp.) revealed by over 10,000 genebank accessions,” Proc. Natl. Acad. Sci., vol. 118, no. 34, p. e2104315118, 2021.
[24] M. S. Islam, “Agro-morphological characterization and genotypic diversity of chilli (Capsicum frutescens) landraces of Bangladesh,” Int. J. Sci. Res. Anal., vol. 12, no. 3, pp. 265–275, 2024.
[25] E. L. Arumingtyas, “Similarity Among 10 Cayenne Pepper (Capsicum frutescens L.) Genotypes Based on Quantitative Characters,” J. Trop. Plant Physiol., vol. 15, no. 2, pp. 89–98, 2023.
[26] A. Supyani, “Determinants of symptom variation of Pepper yellow leaf curl Indonesia virus on chili pepper,” Biodiversitas, vol. 24, no. 3, pp. 1234–1245, 2023.
[27] P. T. et al., “Comparative result analysis of cauliflower disease classification,” Decis. Anal. J., 2025, [Online]. Available: https://doi.org/10.1016/j.dajour.2025.100552
[28] N. E. K. et al., “Deep Learning Models Comparisons on Rice Image Disease Classification,” IEEE Access, 2025, [Online]. Available: https://doi.org/10.1109/ACCESS.2025.10842812
[29] V. T. et al., “VGG-EffAttnNet: Hybrid Deep Learning Model for Chili Plant Disease Dataset,” Front. Plant Sci., 2025, [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC12288623/
[30] J. Haines, “Call for emergency action to limit global temperature increases, restore biodiversity, and protect health,” J. Heal. Popul. Nutr., vol. 40, no. 1, p. 25, 2021, [Online]. Available: https://jhpn.biomedcentral.com/articles/10.1186/s41043-021-00262-x
