Putu Sugiartawan, Nobuo Funabiki, I Nyoman Darma Kotama, Amma Liesvarastranta Haz, Komang Candra Brata, Ni Wayan Wardani
Nowadays, dried Moringa leaves (M. oleifera) are increasingly in demand due to their health benefits. High-quality ones have shown remarkable positive effects as antioxidants, antidiabetics, and anti-inflammatory agents. However, in the industry, the quality classification process into six categories is performed manually by farmers, which is time-consuming and error-prone. Particularly, the two highest categories of Class A and Class B are hard to distinguish, since they are visually similar. In this paper, to automate the classification process, we introduce a new high-resolution dataset, extract color and texture features using the Gray-Level Co-occurrence Matrix (GLCM) method, and present a two-stage classification method using the Light Gradient Boosting Machine (LightGBM) algorithm with them. The experimental results show that the proposal improved classification accuracy from 82% by the baseline algorithm to 90% while maintaining high processing efficiency, demonstrating its potential for real-time and scalable industrial applications in dried Moringa leaves quality grading. © 2025 by the authors.
Department of Information and Communication Systems, Okayama University, Okayama, 700-8530, Japan; Department of Informatics, Institut Bisnis dan Teknologi Indonesia, Denpasar, 80225, Indonesia; Department of Informatics Engineering, Universitas Brawijaya, Malang, 65145, Indonesia
