Plant Disease Detection Using Deep Learning

Plant diseases pose a significant threat to global food security, causing substantial economic losses to farmers and disrupting agricultural ecosystems. Early and accurate detection of plant diseases is critical for timely intervention and crop management. Traditional methods of disease identification rely on visual inspection by agricultural experts, which is time-consuming, expensive, and often inaccessible to small-scale farmers in rural areas.

This project presents the design and development of an automated Plant Disease Detection System using deep learning techniques. A Convolutional Neural Network (CNN) model based on the VGG16 architecture with transfer learning is trained on the PlantVillage dataset comprising over 87,000 images spanning 38 classes of healthy and diseased plant leaves across 14 crop species including tomato, potato, corn, grape, apple, and others.

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