Deep Learning based automated wheat disease diagnosis system

A deep learning-based automated wheat disease diagnosis system leverages advanced neural network architectures to analyze images of wheat plants and accurately identify diseases at various stages. By utilizing convolutional neural networks (CNNs), the system can process high-resolution images captured in the field, detecting visual symptoms such as discoloration, lesions, and blight. The training dataset comprises a diverse range of images representing healthy and diseased plants, enabling the model to learn distinct features associated with each condition. Once trained, the system provides real-time diagnostics, allowing farmers to make informed decisions about disease management and intervention. This approach not only enhances the efficiency of disease detection but also promotes sustainable agricultural practices by minimizing crop losses and reducing the reliance on chemical treatments. Ultimately, such a system contributes to improved food security and higher yields in wheat production.