Generative Adversarial Network-based Augmented Rice Leaf Disease Detection using Deep Learning

Rice is one of the most produced crops in the world and the staple food in many South Asian countries. Rice leaf disease can affect the production of rice vastly, which can be prevented through the early detection of it. Many machine learning techniques have been used in recent years to help in the prevention of one of the most serious concerns, which is disease transmission.But there are limited images available of diseased leaf compared to healthy images which makes life tougher for machine learning models as they need a good amount of data for training. To solve this problem, a Generative adversarial network(GAN) has been used in recent days to create new, synthetic instances of an image that can pass as a real image. Recently, it has been used widely in the field of leaf disease identification. But there is very limited work done on rice diseases. In this paper, SRGAN (Super Resolution-GAN) has been considered as a data augmentation method to balance the dataset. Afterward, DenseNet121, DenseNet169, MobileNetV2, and VGG16 have been applied to classify the diseases. Experiment results show that the newly created augmented dataset produces the best results with both DenseNet169 and moboleNetV2 when compared to other models, with high accuracy of 94.30.