The Plant’s Leaf Identification using G-A-N and Using IA Technique

The identification of the disease of paddy leaves is a very critical issue, as it is quite hard with the naked eye, and even professionals like agricultural scientists may make mistakes in this regard. This is a problem that our approach, which we undertake in this paper, may remedy. In this paper, we present deep learning with the use of generative adversarial networks in leaf disease diagnosis using artificial intelligence. In our experiment, we used a dataset with 12,880 paddy leaf images produced by Cycle GAN, displaying five main disease classifications. GANs should be taken into consideration while data augmentation is performed to prevent overfitting, while a GAN design that uses two networks-a Generator and a Discriminator-is suggested. The generator was taught to produce samples of data that are comparable to the original data. The deep learning proposed approach can be regarded as an important tool for early disease identification in the detection and classification of paddy leaf diseases.