Authors - Min Wai Yan Oo, Jirarat Sitthiworachart Abstract - Plant diseases pose a major threat to agricultural productivity, food security, and the preservation of medicinal plant species. Early and accurate disease identification is essential to minimize crop losses; however, traditional diagnostic methods rely on manual inspection and expert knowledge, which are often time-consuming, expensive, and not easily accessible to farmers in rural areas. To overcome these limitations, this paper proposes a Smart System for Identifying Leaf Disease Detection using Artificial Intelligence (AI) and Computer Vision techniques. The primary objective of the proposed system is to develop an automated, scalable, and web-based solution capable of identifying plant species and detecting leaf diseases through image analysis. The system utilizes Computer Vision algorithms to extract critical visual features such as color variations, texture patterns, and morphological characteristics from uploaded leaf images. A deep learning–based classification model processes these features to determine whether the leaf is healthy or diseased. The frontend interface is developed using React and TypeScript, ensuring an interactive and responsive user experience, while backend AI processing is integrated through secure API services. Experimental evaluation demonstrates high classification accuracy and reliable confidence scores under varying environmental conditions. The system also provides treatment recommendations to promote sustainable agricultural practices. By integrating AI driven analytics with modern web technologies, the proposed system enhances early disease detection, reduces dependency on expert consultation, and contributes to sustainable farming, improved crop management, and digital preservation of medicinal plant knowledge.
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