Deep Learning-Based Herbal Plant Classification Using Leaf Shape and Pattern: The UII Botanical Leaf Dataset
Abstract
Herbal plants play a crucial role in healthcare and are widely used as traditional medicines. However, identifying herbal species remains a major challenge due to morphological similarities, particularly in leaf shape and texture. This study aims to develop an intelligent classification system for Indonesian herbal plants based on leaf image analysis using artificial intelligence (AI) and digital image processing techniques. A localized dataset of 47 herbal species collected from Botanical SmartPark SMA UII was used to train a deep learning model employing the MobileNetV2 architecture through transfer learning. The proposed model achieved an average accuracy of 96.6% on the testing dataset, demonstrating high reliability in recognizing species with complex visual variations. The trained model was then implemented into an Android-based application called HERBfull Botanical SmartPark, enabling real-time plant identification and interactive access to botanical information. The system successfully enhances efficiency, accessibility, and educational value in the identification of local herbal species. This research contributes to the advancement of AI applications in botanical education, promoting digital literacy, biodiversity conservation, and the integration of smart technology into sustainable environmental learning platforms.
References
World Health Organization (WHO), “WHO Traditional Medicine Strategy 2014-2023,” World Heal. Organ., pp. 1–76, 2013, doi: 2013.
J. P. Unggul, “Potensi Tumbuhan yang Berkhasiat Obat Herba di Area Kampus Sekolah Tinggi Pertanian Flores Bajawa,” vol. 3, pp. 80–93, 2024.
F. S. Haba, M. M. E. Purnama, and A. E. Mau, “Keanekaragaman Jenis Dan Pemanfaatan Tumbuhan Obat Di Hutan Penelitian Bu’At So’E, Kecamatan Mollo Selatan, Kabupaten Timor Tengah Selatan, Provinsi Nusa Tenggara Timur,” Wana Lestari, vol. 4, no. 01, pp. 182–193, 2022, doi: 10.35508/wanalestari.v6i01.8041.
F. S. Ni’mah, T. Sutojo, and D. R. I. M. Setiadi, “Identification of Herbal Medicinal Plants Based on Leaf Image Using Gray Level Co-occurence Matrix and K-Nearest Neighbor Algorithms,” J. Teknol. dan Sist. Komput., vol. 6, no. 2, pp. 51–56, 2018, doi: 10.14710/jtsiskom.6.2.2018.51-56.
N. P. D. A. S. Dewi, M. W. A. Kesiman, I. M. G. Sunarya, I. G. A. A. D. Indradewi, and I.G. Andika, “TPHerbleaf: Dataset Untuk Klasifikasi Jenis Daun Tumbuhan Herbal Berdasarkan Lontar Usada Taru Pramana,” J. Resist. (Rekayasa Sist. Komputer), vol. 6, no. 2, pp. 57–68, 2023, doi: 10.31598/jurnalresistor.v6i2.1421.
T. Mandasari, “Potensi Tumbuhan yang Berkhasiat Obat Herba di Area,” Stikes Bhakti Husada Mulia Madiun, Madiun, 2022.
F. S. Ni’mah, T. Sutojo, and D. R. I. M. Setiadi, “Identification of Herbal Medicinal Plants Based on Leaf Image Using Gray Level Co-occurence Matrix and K-Nearest Neighbor Algorithms,” J. Teknol. dan Sist. Komput., vol. 6, no. 2, pp. 51–56, 2018, doi: 10.14710/jtsiskom.6.2.2018.51-56.
D. N. Khaeva and C. Basir, “Implementasi Metode Convolutional Neural Network (CNN) Untuk Mendeteksi Jenis Tanaman Herbal,” J. SAINTEK Patompo, vol. 2, no. 2, pp. 65–73, 2024.
N. Jamil, N. A. C. Hussin, S. Nordin, and K. Awang, “Automatic Plant Identification: Is Shape the Key Feature?,” Procedia Comput. Sci., vol. 76, no. Iris, pp. 436–442, 2015, doi: 10.1016/j.procs.2015.12.287.
E. Elhariri, N. El-Bendary, and A. E. Hassanien, “Plant classification system based on leaf features,” Proc. 2014 9th IEEE Int. Conf. Comput. Eng. Syst. ICCES 2014, pp. 271–276, 2014, doi: 10.1109/ICCES.2014.7030971.
P. Lakshmi Prasanna and D. Rajeswara Rao, “Text classification using artificial neural networks,” Int. J. Eng. Technol., vol. 7, no. 1.1 Special Issue 1, pp. 603–606, 2018, doi: 10.14419/ijet.v7i1.1.10785.
A. K. Mulugeta, D. P. Sharma, and A. H. Mesfin, “Deep learning for medicinal plant species classification and recognition: a systematic review,” Front. Plant Sci., vol. 14, no. January, pp. 1–18, 2023, doi: 10.3389/fpls.2023.1286088.
H. Yalcin and S. Razavi, “Plant classification using convolutional neural networks,” 2016 5th Int. Conf. Agro-Geoinformatics, Agro-Geoinformatics 2016, 2016, doi: 10.1109/Agro- Geoinformatics.2016.7577698.
S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Front. Plant Sci., vol. 7, no. September, pp. 1–10, 2016, doi: 10.3389/fpls.2016.01419.
J. G. Arnal Barbedo, “Plant disease identification from individual lesions and spots using deep learning,” Biosyst. Eng., vol. 180, no. 2016, pp. 96–107, 2019, doi: 10.1016/j.biosystemseng.2019.02.002.
G. Valarmathi, S. U. Suganthi, V. Subashini, R. Janaki, R. Sivasankari, and S. Dhanasekar, “CNN algorithm for plant classification in deep learning,” Mater. Today Proc., vol. 46, no. xxxx, pp. 3684–3689, 2020, doi: 10.1016/j.matpr.2021.01.847.
S. H. Lee, C. S. Chan, S. J. Mayo, and P. Remagnino, “How deep learning extracts and learns leaf features for plant classification,” Pattern Recognit., vol. 71, pp. 1–13, 2017, doi: 10.1016/j.patcog.2017.05.015.
M. Dyrmann, H. Karstoft, and H. S. Midtiby, “Plant species classification using deep convolutional neural network,” Biosyst. Eng., vol. 151, no. 2005, pp. 72–80, 2016, doi: 10.1016/j.biosystemseng.2016.08.024.
