Enhancing Groundnut Leaf Disease and Stress Detection Using GAN-Based Deep Learning Models

Global food security is greatly influenced by agriculture and crop productivity and quality can only be maximized by early diagnosis of plant diseases. Manual inspection is the foundation of traditional disease detection techniques and it is labor intensive, time consuming and error prone. With the development of deep learning and Generative Adversarial Networks (GANs) automated plant disease and stress detection has taken a revolutionary turn. In order to improve the precision and resilience of groundnut plant leaf disease and stress detection this study investigates the use of GANs. To enhance datasets GANs produce high quality synthetic images which helps models to understand better the intricate illness patterns in a variety of settings. Moreover GAN based detection enhances early disease detection minimizing financial losses and maximizing agricultural output.