TRANSFER LEARNING-BASED CLASSIFICATION OF BELL PEPPER LEAF DISEASES USING VGG16 AND EFFICIENTNETB3 ARCHITECTURES
DOI:
https://doi.org/10.33480/jitk.v11i3.7913Keywords:
CNN, EfficientNetB3, Ensemble Soft Voting, Pepper leaf classification, Transfer learningAbstract
Diseases affecting pepper leaves can significantly reduce crop productivity and quality, while manual disease identification remains subjective, time-consuming, and prone to error. Therefore, an accurate automated classification system is required to support early disease detection. This study aims to evaluate and compare the performance of a conventional Convolutional Neural Network (CNN) with two transfer learning–based architectures, VGG16 and EfficientNetB3, for classifying pepper leaf images into healthy and bacterial spot classes, as well as to analyze the impact of applying a soft voting ensemble method on classification performance. The dataset was obtained from Kaggle and divided into training, validation, and test sets. Image preprocessing included resizing all images to 224×224 pixels and applying data augmentation to improve model generalization. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results indicate that EfficientNetB3 outperforms the conventional CNN and VGG16 models. Furthermore, the application of the soft voting ensemble enhances prediction stability, achieving an accuracy of 99.68% on the test dataset with balanced precision and recall across both classes. These findings demonstrate that the integration of transfer learning and soft voting ensemble methods is an effective approach for image-based pepper leaf disease classification under the experimental conditions, and provides a basis for further validation using more diverse datasets.
Downloads
References
[1] N. Inaya, S. Meriem, and M. Masriany, “Identifikasi morfologi penyakit tanaman cabai (Capsicum sp.) yang disebabkan oleh patogen dan serangan hama lingkup kampus UIN Alauddin Makassar,” Filogeni J. Mhs. Biol., vol. 2, no. 1, pp. 8–14, 2022, doi: 10.24252/filogeni.v2i1.27092.
[2] S. N. Nugraha, R. Pebrianto, and E. Fitri, “Penerapan Deep Learning Pada Klasifikasi Tanaman Paprika Berdasarkan Citra Daun Menggunakan Metode CNN,” Inf. Syst. Educ. Prof. J. Inf. Syst., vol. 8, no. 2, p. 15, 2023, doi: 10.51211/isbi.v8i2.2671.
[3] P. M. A. Reza, S. Syuhriatin, and S. M. Rahayu, “Analisis Pertumbuhan Tanaman Paprika (Capsicum annuum var. grossum) Berdasarkan Pola Tanam,” Lomb. J. Sci., vol. 3, no. 1, pp. 23–32, 2021.
[4] Irfandri, A. Hamzah, R. Rustam, H. Fauzana, and A. Effendi, “Development of Cayenne Pepper Plants for Improvement Family Economy in Koto Parambahan Village Kampar District Kampar Regency,” JCSPA J. Community Serv. Public Aff., vol. 1, no. 2, pp. 45–50, 2021.
[5] Febrica Handryani, S. Nur Wiyono, K. Kusno, and D. Rochdiani, “Identifikasi Risiko Pada Produksi Paprika (Studi Kasus di CV Cantigi Kabupaten Garut, Jawa Barat),” Forum Agribisnis, vol. 11, no. 1, pp. 90–100, 2021, doi: 10.29244/fagb.11.1.90-100.
[6] V. Salim, A. Abdullah, and P. Y. Utami, “Klasifikasi Citra Penyakit Tanaman Pada Daun Paprika Dengan Metode Transfer Learning Menggunakan DenseNet-201,” Indones. J. Comput. Sci., vol. 12, no. 2, pp. 284–301, 2023, [Online]. Available: http://ijcs.stmikindonesia.ac.id/ijcs/index.php/ijcs/article/view/3135
[7] Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects,” IEEE Trans. Neural Networks Learn. Syst., vol. 33, no. 12, pp. 6999–7019, 2022, doi: 10.1109/TNNLS.2021.3084827.
[8] Y. Y. Pane and J. J. Sihombing, “Klasifikasi Jenis Burung menggunakan Metode Transfer Learning,” J. Teknol. Terpadu, vol. 9, no. 2, pp. 89–94, 2023, doi: 10.54914/jtt.v9i2.744.
[9] A. Mufidatuzzainiya and M. Faisal, “Penggunaan Teknik Transfer Learning pada Metode CNN untuk Pengenalan Tanaman Bunga,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 10, no. 2, pp. 195–206, 2025, doi: 10.14421/jiska.2025.10.2.195-206.
[10] E. Setia Budi, A. Nofriyaldi Chan, P. Priscillia Alda, and M. Arif Fauzi Idris, “Optimization of machine learning model for image classification and prediction using convolutional neural network algorithm,” Media Online, vol. 4, no. 5, p. 509, 2024, [Online]. Available: https://djournals.com/resolusi
[11] Z. P. Jiang, Y. Y. Liu, Z. E. Shao, and K. W. Huang, “An improved VGG16 model for pneumonia image classification,” Appl. Sci., vol. 11, no. 23, 2021, doi: 10.3390/app112311185.
[12] P. Oza, P. Sharma, and S. Patel, “a Transfer Representation Learning Approach for Breast Cancer Diagnosis From Mammograms Using Efficientnet Models,” Scalable Comput., vol. 23, no. 2, pp. 51–58, 2022, doi: 10.12694/scpe.v23i2.1975.
[13] A. G. Costa Junior, F. S. da Silva, and R. Rios, “Deep Learning-Based Transfer Learning for Classification of Cassava Disease,” pp. 364–375, 2025, doi: 10.5753/eniac.2024.244378.
[14] A. C. Anwari, “Klasifikasi Penyakit Tanaman Tomat Melalui Citra Daun Menggunakan Metode Convolutional Neural Network,” JIMUJurnal Ilm. Multidisipliner, vol. 2, no. 03, pp. 639–647, 2024, doi: 10.70294/jimu.v2i03.421.
[15] M. Wahyu, N. Nurlina, and D. Irawan, “Multitek Indonesia : Jurnal Ilmiah Multitek Indonesia : Jurnal Ilmiah,” Multitek Indones. J. Ilm., vol. 17, no. 1, pp. 60–68, 2023.
[16] E. L. P. Ristanti, “Analisis Dan Perbandingan Arsitektur Vgg16 Dan Mobilenetv2 Untuk Klasifikasi Dan Identifikasi Penyakit Daun Pada Tanaman Cabai Menggunakan Cnn,” Sci. J. Ilm. Sains dan Teknol., vol. 2, no. 9, pp. 216–226, 2024, [Online]. Available: https://doi.org/10.572349/scientica.v2i9.2381
[17] G. Sharma, V. Anand, S. Malhotra, S. Kukreti, and S. Gupta, “CNN-Based Intelligent Classification of Bell Pepper Plant Diseases: A Novel Approach for Precision Agriculture,” in 2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), 2023, pp. 1–5. doi: 10.1109/SMARTGENCON60755.2023.10442079.
[18] M. P. Mathew, K. M. Abubeker, and S. B. Nuthalapati, “Analysis of Pepper Leaf Diseases Based on Bell CNN Architecture BT - Fifth Congress on Intelligent Systems,” S. Kumar, E. A. Mary Anita, J. H. Kim, and A. Nagar, Eds., Singapore: Springer Nature Singapore, 2025, pp. 159–171.
[19] T. D. Salka, M. B. Hanafi, S. M. S. A. A. Rahman, D. B. M. Zulperi, and Z. Omar, “Plant leaf disease detection and classification using convolution neural networks model: a review,” Artif. Intell. Rev., vol. 58, no. 10, 2025, doi: 10.1007/s10462-025-11234-6.
[20] P. Kumar, Pooja, N. Chauhan, and N. Chaurasia, “A Vision-Based Pothole Detection Using CNN Model,” SN Comput. Sci., vol. 4, no. 6, p. 716, 2023, doi: 10.1007/s42979-023-02153-w.
[21] T. Nguyen, T. Nguyen, and B.-V. Ngo, “A VGG-19 Model with Transfer Learning and Image Segmentation for Classification of Tomato Leaf Disease,” AgriEngineering Artic., vol. 4, no. 4, pp. 871–887, 2022.
[22] Ü. Atila, M. Uçar, K. Akyol, and E. Uçar, “Plant leaf disease classification using EfficientNet deep learning model,” Ecol. Inform., vol. 61, p. 101182, 2021, doi: https://doi.org/10.1016/j.ecoinf.2020.101182.
[23] S. Kumari, D. Kumar, and M. Mittal, “An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier,” Int. J. Cogn. Comput. Eng., vol. 2, no. November 2020, pp. 40–46, 2021, doi: 10.1016/j.ijcce.2021.01.001.
[24] A. A. A. El-aziz, M. A. Mahmood, and S. A. El-Ghany, “EfficientNet-B3-Based Automated Deep Learning Framework for Multiclass Endoscopic Bladder Tissue Classification,” diagnostics, vol. 15, no. 19, pp. 1–38, 2025.
[25] M. Shoaib et al., “An advanced deep learning models-based plant disease detection: A review of recent research,” Front. Plant Sci., vol. 14, no. March, pp. 1–22, 2023, doi: 10.3389/fpls.2023.1158933.
[26] G. Kunapuli, Ensemble methods for machine learning. Simon and Schuster, 2023.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Siti Nurhasanah Nugraha, Evita Fitri, Muji Ernawati
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
