Deep Learning-Based Classification of Paddy Crop Diseases Using a Custom Image Dataset

Plant diseases pose significant threats globally due to the high economic losses and effects on food security. Traditional disease identification methods usually have limitations regarding their accuracy and efficiency. This study discusses six advanced deep learning models: VGG19, DenseNet201, Xception, InceptionResNetV2, MobileNetV2, and EfficientNetV2B3. A dataset is used that is rich in diversity and contains high-quality images of diseased sections or parts of plants. These deep models are discussed and compared for studying their efficiencies in recognizing plant diseases accurately. EfficientNetV2B3 and Xception outperformed the rest of the models due to the ability of the model to capture major features from the image of the infected region. MobileNetV2 was also useful which provided a good trade-off between accuracy and computational efficiency. The study further applied transfer learning and image augmentation in boosting model performance and addressing the issue of class imbalance in the dataset. Results showed that the proposed approach proved much more reliable and efficient compared to conventional approaches to plant disease detection. Future efforts will be geared towards early detection of diseases to further assist farmers and researchers in order to upgrade the practices related to crop management. Additional data will be integrated, including hyperspectral images and environmental factors, for developing a robust and efficient system for plant disease detection. These models will be deployed in intelligent farming systems.