Plant Disease Detector with Image Upload and Machine Learning

Plant diseases have become a major concern in agriculture due to changing climatic conditions, pest attacks, and improper crop management practices. Early detection of plant diseases is essential to prevent yield loss, reduce excessive pesticide usage, and maintain crop health. Traditional disease identification methods rely on manual inspection by farmers or experts, which can be time-consuming, subjective, and difficult to perform at scale. This paper presents a Plant Disease Detector, a software system designed to identify plant leaf diseases using image processing and machine learning techniques. The system analyzes uploaded or captured leaf images and extracts important visual features such as color statistics, texture patterns, and Gray Level Co-occurrence Matrix (GLCM) characteristics. A Support Vector Machine (SVM) classifier trained on labeled leaf image datasets predicts disease categories efficiently and classifies leaves as healthy or diseased, identifying the specific disease type with treatment recommendations..