Plant Health AI Analyzer Using Supervised Learning

Plant diseases significantly impact agricultural productivity, resulting in significant financial losses and food shortages. Conventional disease detection techniques depend on labor-intensive and frequently imprecise manual inspection. This study introduces a Plant Health AI Analyzer that automatically recognizes and categorizes plant leaf diseases from photos using supervised machine learning methods. Utilizing image enhancement and segmentation techniques, a dataset of both healthy and diseased plant leaves was gathered and preprocessed. Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), two supervised learning models, were used to extract and train features like color, texture, and shape. According to experimental results, the suggested methodology outperforms conventional inspection techniques in the classification of plant diseases, with an accuracy of 94.8%. By helping farmers with early diagnosis and treatment, the AI analyzer can encourage sustainable agriculture and lessen the usage of pesticides. The model's integration into a mobile-based IoT platform for real-time field diagnosis will be the main focus of future research.