Improving losses & accuracy through design of deep convolutional generative adversarial network (DCGAN) for plant disease detection tasks

Rice leaf diseases are a significant issue that adversely impact rice production in India. Identifying these diseases manually is labour-intensive and prone to delays, often resulting in substantial crop losses for farmers. Therefore, the need for an automated system for early detection of plant diseases is critical. Recent advancements in machine learning, computer vision, and deep learning have paved the way for classification models capable of automatically identifying these diseases. However, the challenge lies in obtaining a sufficiently large and diverse image dataset to effectively train deep learning models. In this paper, we address this limitation by employing advanced data augmentation techniques, including Deep Convolutional Generative Adversarial Networks (DCGANs), to generate synthetic images that expand the dataset of rice leaf diseases. By integrating these synthetic images with real images, a new Convolutional Neural Network (CNN) architecture is proposed, which offers improved generalization capabilities. The performance of the classification model is evaluated with and without the DCGAN-generated images. The results demonstrate that the inclusion of synthetic images significantly enhances accuracy, as the enlarged dataset better represents real-world conditions. This approach provides a promising solution for more effective rice disease identification, offering higher precision in real-time scenarios.