IDENTIFICATION and MEDICINAL ASSESSMENT of INDIGENOUS MEDICINAL PLANT LEAVES USING IMAGE PROCESSING TECHNIQUES

Abstract

Indigenous medicinal plants hold immense therapeutic potential, yet their systematic identification and medicinal assessment remain underexplored. This study addresses the research gap by investigating the integration of traditional knowledge with modern technology to identify and assess the medicinal properties of plant species in the Yem Special Zone, Ethiopia. A novel Medicinal Assessment Framework (MAF) is developed using image processing techniques and machine learning classification to bridge the gap between traditional practices and contemporary scientific methodologies. The study employs a Design Science Research Methodology (DSRM), involving iterative cycles of development, evaluation, and refinement. Key steps include ethnobotanical data collection, digitization of traditional knowledge, acquisition and preprocessing of plant leaf images, feature extraction (shape, color, and texture), machine learning-based classification, and medicinal assessment scoring. Advanced image processing techniques such as Gaussian blur, Otsu’s thresholding, and morphological operations are used, while machine learning classifiers assess the extracted features. The results demonstrate the efficacy of the framework, achieving satisfactory accuracy in classifying 25 indigenous plant species based on leaf characteristics. The medicinal assessment scoring system combines traditional knowledge with quantitative analysis, offering a comprehensive evaluation of each species' medicinal potential. This integrative approach not only enhances the identification process but also contributes to the preservation and sustainable use of indigenous medicinal plants. In conclusion, the proposed framework provides a valuable tool for researchers, conservationists, and healthcare practitioners, promoting interdisciplinary research and community engagement. This work underscores the importance of leveraging traditional knowledge alongside cutting-edge technologies to preserve cultural heritage and biodiversity while advancing medicinal research.