Enhancing Rice Disease Binary Classification. An Analysis of Deep Neural Network Models

The study examines deep neural network models for the binary classification of rice plants, with a specific emphasis on distinguishing between healthy and ill states. The utilization of convolutional neural network designs such as VGG16, Inception V3, ResNet18, and MobileNet is commonly observed. The study gathered a sample of 500 photographs depicting both good and unhealthy conditions in Kathmandu and Sindhupalchowk. The objective was to evaluate the efficacy of the models in generalizing the health states of rice. The results indicated that model Resnet exhibited superior performance with a high level of accuracy, whereas model VGG and Inception had lower accuracy. The model exhibits promising potential in influencing the diagnosis and early detection of rice diseases.