Deep Learning Approach for Rice Leaf Disease Detection Using CNN Models

Growing and growing rice is important for keeping the world's food supply safe, but the chance of diseases spreading to rice fields makes things very hard. These diseases can be caused by many things, such as bacteria, fungi, viruses, and external stresses. The main way that these diseases are found is by analyzing the leaves. We got 711 pictures that show five types of different diseases. After adding more pictures, the dataset grows to 4449 pictures that show the same five types of diseases: bacterial leaf blight, brown spot, hispa, leaf smut, and tungro. Because of these problems, researchers are looking into how to use new technologies, especially Convolutional Neural Networks (CNNs), to find rice leaf diseases more accurately. We came up with four deep learning models for this study: EfficientNetB7, VGG-19, ResNet-101, and InceptionResNetV2. A certain number of epochs were used to train each model, and different performance measures were looked at, such as training accuracy, validation accuracy, validation loss, and total model accuracy. The test results show that EfficientNetB7 and ResNet-101 work better than other models, getting amazing accuracy, recall, and F1-scores across a number of disease categories. EfficientNetB7 and ResNet-101 are the best, with an accuracy rate of 99%, validation accuracy of 99.44% and 99.58%, and the lowest validation loss of 0.0151 for EfficientNetB7 and 0.0279 for ResNet-101.