This repository contains an implementation of a deep learning model using the ResNet architecture in PyTorch for detecting diseases in corn leaves. The model is trained to classify images of corn leaves into different disease categories, helping farmers identify and address potential issues in their crops at an early stage.
The dataset used for training and evaluation consists of a large collection of labeled images of corn leaves affected by various diseases, including common issues such as rust, blight, and leaf spot. The dataset is carefully curated and annotated to ensure accurate classification.
The model is built using the ResNet (Residual Network) architecture, which is a powerful deep convolutional neural network known for its excellent performance in image classification tasks. ResNet has demonstrated superior performance in various computer vision challenges, making it a suitable choice for disease detection in corn leaves.
