Deep Learning Based Leaf Disease Detection

Abstract

The rising prevalence of plant diseases poses a significant challenge to agricultural productivity and food security. This project introduces a deep learning-based solution for leaf disease detection, utilizing Convolutional Neural Networks (CNN) and transfer learning techniques to improve diagnostic accuracy. By leveraging pre-trained models, we reduce the reliance on large training datasets while maintaining high classification performance. A comprehensive dataset of both healthy and diseased leaf images is collected, with advanced image preprocessing and augmentation methods employed to enhance the model's robustness. Our experimental findings demonstrate that the proposed method effectively identifies a range of leaf diseases, showing substantial improvements in accuracy and efficiency over traditional diagnostic approaches. The use of transfer learning not only accelerates the training process but also boosts the model's ability to generalize across various plant species and disease conditions. This research underscores the importance of deep learning in precision agriculture, providing an innovative tool for early disease detection that enables farmers to take proactive action, thus reducing crop losses and promoting sustainable farming practices.