Application of Digital Image Processing for Orchid Image Segmentation in Morphological Plant Analysis - Riza, Bob Subhan; Rosnelly, Rika; Haryanto S., Edy Victor (Journal of Applied Science, Engineering, Technology, and Education, 2025)
The deployment of digital image processing in orchid image segmentation for plant morphological analysis is investigated in this study. The goal of this study is to increase the accuracy of orchid species identification using color-based segmentation approaches using 90 photos of three different orchid species—Cattleya, Dendrobium, and Onchidium—that were retrieved from Kaggle. Pre-processing is the first step in the process, which involves shrinking the size of the photos, separating them into RGB components, and converting them to HSV color space for additional analysis. Segmentation is done using the K-Means technique, which clusters pixels according to the color features that have been retrieved. Centroid updates are made until convergence is reached. With an identification accuracy of 92%, the binary and RGB segmentation results show how well this method works to distinguish the flower item from the backdrop. By advancing image processing methods in botany, this study aids in the identification of rare orchid species and conservation initiatives.