Plant Leaf Disease Detection Using Convolutional Neural Network

In agriculture, the early detection and management of plant diseases are crucial for ensuring crop health and yield. This project presents an innovative approach utilizing deep learning techniques for the automated detection and classification of plant diseases. Leveraging convolutional neural networks (CNNs) implemented in the Pytorch framework, we develop a robust system capable of accurately classifying leaf images into 39 different disease categories. The model is trained on the Plant Village dataset, a comprehensive collection of annotated images representing various plant diseases. By harnessing the power of deep learning, our solution offers farmers an efficient tool for timely diagnosis and intervention, ultimately aiding in the preservation of crop health and agricultural productivity. The project's code and resources, including the dataset link, are made accessible through our blog section, facilitating reproducibility and further research in this field.