In this project, a deep learning-based classification model was developed to detect diseases in rice leaves using a Convolutional Neural Network (CNN). The primary goal of the project was to create an efficient and accurate model that can automatically identify various types of diseases affecting rice crops, thereby assisting farmers in taking timely actions to protect their yields.
This model can detect ['Bacterialblight', 'Blast', 'Brownspot', 'Tungro'] these 4 diseases.
The dataset for this project consisted of images of rice leaves, categorized into different classes based on the presence of specific diseases, such as bacterial blight, brown spot, and leaf smut, among others. The images were preprocessed through techniques such as normalization and augmentation to enhance the model's generalization capabilities and reduce overfitting.