Machine Learning Based Plant Leaf Disease Detection Using CNN

Leaf diseases are a major factor affecting agricultural productivity and crop quality worldwide. Early detection of plant diseases is essential to prevent large-scale losses and improve yield. Traditional disease detection methods rely on manual inspection, which is time-consuming, labour-intensive, and less accurate. This paper presents a machine learning-based plant leaf disease detection system using Convolutional Neural Networks (CNN). The proposed system captures leaf images and processes them using image processing techniques. A trained CNN model is used to classify diseases based on features such as color, texture, and shape. The system is capable of identifying diseases like bacterial blight, rust, and leaf spot with high accuracy. The proposed approach reduces human effort, enables real-time monitoring, and supports precision agriculture.