Image Classification for Rice Leaf Disease Using AlexNet Model

As there are many countries that consume rice, rice is one of the most cultivated crops in the world. In order to grow rice successfully, it is necessary to understand the stages of rice cultivation, as the proposed system as to know about various diseases. In recent years, researchers have developed various diagnostic methods for rice leaf disease. Among the many diagnosis methods, the classification methods in deep learning can distinguish rice leaf diseases well and accurately. A classification method based on convolution neural network architectures is defined to classify diseases by taking images of rice leaves from rice fields. AlexNet model is one of the models of Convolutional Neural Network. Using the AlexNet model, rice leaf diseases are classified as Brown leaf spot, Bacterial Leaf blight, Leaf smut and Healthy. In this paper, we test the dataset with a pre_trained model named AlexNet model and show the results. In addition, we also test and compare the results with another CNN model, VGG_16. By analyzing this data based on a Deep Learning model, the proposed system has tried to classify the diseases of rice plant leaf.