Herbal Leaf Classification Using Convolutional Neural Network (CNN) Method With VGG16 Architecture
DOI:
https://doi.org/10.25299/itjrd.2025.24574Keywords:
Image Classification, Herbal Leaves, Convolutional Neural Network, VGG16, Fine-TuningAbstract
Visual identification of various types of herbal leaves such as sweet leaf, moringa leaves, cat whiskers, bay leaf, and betel leaf is often difficult due to morphological similarities. This study presents a novel approach using a Convolutional Neural Network (CNN) model with the VGG16 architecture to automatically classify these five types of leaves. The main novelty of this study lies in the implementation of a two-stage fine-tuning strategy specifically tailored to the herbal leaf dataset. The first stage freezes the base layer and trains a new classification head, while the second stage fine-tunes several upper layers to improve model adaptability. The model was trained using 500 herbal leaf images and evaluated on 235 independent test images. The results demonstrated superior model performance with an overall accuracy rate of 91.06% and an average F1-score of 0.91. Qualitative analysis demonstrated the model's success in classifying leaves with unique features, such as cats whiskers and betel leaf. However, the model faced challenges in distinguishing leaves with high visual similarities, such as sweet leaf and moringa leaves. Practically, the developed model offers an effective and reliable solution for herbal leaf identification, reducing time and error rates compared to manual methods. Although this study is limited by the small dataset size, these results demonstrate the great potential of the optimized VGG16 architecture for applications in botany and traditional medicine, making it a valuable tool.
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[1] S. S. Tahoangako, D. Santosa, and N. Fakhrudin, “Study of the Utilization of Medicinal Plants by Traditional Healer of the Tolaki Ethnic Tribe, Southeast Sulawesi, Indonesia,” Ethnobot. Res. Appl., vol. 28, 2024, doi: 10.32859/era.28.39.1-17.
[2] E. Singarimbun, E. Elfrida, and I. Indriaty, “Indigenous Knowledge and Herbal Medicine: Exploring the Ethnobotany of the Karo Tiganderket Tribe in Indonesia,” Heca J. Appl. Sci., vol. 2, no. 2, pp. 74–86, 2024, doi: 10.60084/hjas.v2i2.208.
[3] F. Azzahra, A. D. Astuti, B. Arifin, and G. Alam, “Scoping Review: Study of Herbs Consumption for Self-Medication in Indonesia 2019-2022,” Maj. Obat Tradis., vol. 29, no. 3, pp. 302–326, 2024, doi: 10.22146/mot.94091.
[4] S. A. E. ALBAKIA and R. A. Saputra, “Identifikasi Jenis Daun Tanaman Obat Menggunakan Metode Convolutional Neural Network (CNN) Dengan Model VGG16,” J. Inform. Polinema, vol. 9, no. 4, pp. 451–460, 2023, doi: 10.33795/jip.v9i4.1420.
[5] A. I. Sulaiman, S. Prastyanti, L. K. Sari, A. Rofik, and M. Yamin, “Community Empowerment in Developing Family Medicinal Plants to Realize a Green Economy Based on Local Wisdom,” no. 9, pp. 54–65, 2025, doi: 10.53935/jomw.v2024i4.1158.
[6] K. Saputra S, D. Niska, I. Taufik, M. Hidayat, and D. Dharma, “Classification of Herbal Plants Based on Leaf Images using Convolutional Neural Network,” 2022, doi: 10.4108/eai.11-10-2022.2325271.
[7] S. Salsabila, A. Suharso, and P. Purwantoro, “Comparison of Deep Learning Architectures in Identifying Types of Medicinal Plant Leaf Images,” J. Appl. Informatics Comput., vol. 8, no. 1, pp. 39–46, 2024, doi: 10.30871/jaic.v8i1.6289.
[8] N. L. Marpaung, R. J. H. Butar Butar, and S. Hutabarat, “Implementasi Deep learning untuk Identifikasi Daun Tanaman Obat Menggunakan Metode Transfer learning,” J. Edukasi dan Penelit. Inform., vol. 9, no. 3, p. 348, 2023, doi: 10.26418/jp.v9i3.63895.
[9] Z. Chen, “Diffusion Models-based Data Augmentation for the Cell Cycle Phase Classification,” J. Phys. Conf. Ser., vol. 2580, no. 1, 2023, doi: 10.1088/1742-6596/2580/1/012001.
[10] A. Mumuni and F. Mumuni, “Data augmentation: A comprehensive survey of modern approaches,” Array, vol. 16, no. November, p. 100258, 2022, doi: 10.1016/j.array.2022.100258.
[11] M. H. Ahmad, M. Hana, T. Ghazi Pratama, and H. Aulida, “Klasifikasi Empat Jenis Daun Herbal Menggunakan Metode Convolutional Neural Network,” J. Ilmu Komput. dan Matemtika, vol. 4, no. 2, pp. 69–76, 2023.
[12] O. E. Putra and R. Permana, “Hybrid Data Mining For Member Determination And Financing Prediction In Syariah Financing Saving And Loan Cooperatives,” J. RESTI, vol. 8, no. 2, pp. 309–320, 2024, doi: 10.29207/resti.v8i2.5683.
[13] I. R. Ramadhani, A. Nilogiri, and A. Qurrota, “Klasifikasi jenis tumbuhan berdasarkan citra daun menggunakan metode convolutional neural network,” J. Smart Teknol., vol. 3, no. 3, pp. 249–260, 2022, [Online]. Available: http://jurnal.unmuhjember.ac.id/index.php/JST
[14] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–14, 2015.
[15] N. N. Sinaga and A. Sembiring, “Klasifikasi Tumbuhan Obat Berdasarkan Citra Daun Menggunakan Algoritma CNN Classification of Medicinal Plants Based on Leaf Images Using CNN Algorithm,” vol. 5, no. April, pp. 64–74, 2025, doi: 10.34007/incoding.v5i1.833.
[16] W. Liu et al., “Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: From convolutional neural networks to visual transformers,” Comput. Biol. Med., vol. 141, 2022, doi: 10.1016/j.compbiomed.2021.105026.
[17] J. Kim, “PadChannel: Improving CNN Performance through Explicit Padding Encoding,” 2023, [Online]. Available: http://arxiv.org/abs/2311.07623
[18] T. M. Arkan, A. Sugiharto, and H. A. Wibawa, “Optimizing VGG16 Architecture with Bayesian Hyperparameter Tuning for Tomato Leaf Disease Classification,” J. Masy. Inform., vol. 16, no. 2, pp. 162–173, 2025, doi: 10.14710/jmasif.16.2.73168.
[19] M. A. Rofiqul Zein, B. Rahmat, and E. Y. Puspaningrum, “Influence of Input Image Size Variations and Data Balancing on VGG-16 and VGG-16-ELM Models for Pneumonia Classification,” J. Artif. Intell. Eng. Appl., vol. 4, no. 3, pp. 2344–2352, 2025, doi: 10.59934/jaiea.v4i3.1166.
[20] S. Sathyanarayanan, “Confusion Matrix-Based Performance Evaluation Metrics,” African J. Biomed. Res., vol. 27, no. 4, pp. 4023–4031, 2024, doi: 10.53555/ajbr.v27i4s.4345.
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