Plant Disease Detection from Image Using CNN

Abstract

The increasing threat of plant diseases  poses a significant challenge to global food security. Rapid  and accurate identification of plant diseases is crucial for  effective disease management and prevention. In recent  years, deep learning techniques have shown great promise  in automating the process of plant disease identification  through image analysis. This report presents a  comprehensive study on image-based plant disease  classification using deep learning techniques. The report  begins by providing an overview of plant diseases and their  impact on agriculture. It discusses the limitations of  traditional disease identification methods and highlights  the potential of deep learning algorithms in revolutionizing  the field. The importance of image-based approaches is  emphasized due to their non-destructive and scalable  nature. 

Next, the report delves into the methodology of deep  learning for plant disease classification. It explores various  architectures such as convolutional neural networks  (CNNs) and their variants, including transfer learning and  ensemble methods. The training process, data  augmentation techniques, and hyperparameter tuning are  discussed in detail.