Leaf Disease Detection System

Abstract

Abstract: The agricultural sector plays a crucial role in sustaining the world's growing population. However, the prevalence of plant leaf diseases can significantly impact crop yields and quality. This project leverages the power of deep learning techniques to develop an automated system for the early detection of plant leaf diseases. By utilizing a large dataset of annotated leaf images and state-of-the-art convolutional neural networks (CNNs), this research aims to accurately identify and classify various plant leaf diseases, including but not limited to fungal, bacterial, and viral infections. In this project, we propose an innovative approach to tackle this issue. Specifically, we utilize convolutional neural networks (CNNs) to analyze images of plant leaves and classify them into healthy or diseased categories. To ensure the robustness of our model, we curate an extensive dataset comprising images of various plant diseases and healthy leaves. This project stands to benefit the agriculture industry by offering a cost-effective, scalable, and timely solution for plant disease detection.