A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant

Abstract

Leaf blight spot disease, caused by bacteria and fungi, poses a considerable threat to commercial plants, manifesting as yellow to brown color spots on the leaves and potentially leading to plant mortality and reduced agricultural productivity. The susceptibility of jasmine plants to this disease emphasizes the necessity for effective detection methods. In this study, we harness the power of a deep convolutional generative adversarial network (DCGAN) to generate a dataset of jasmine plant leaf disease images. Leveraging the capabilities of DCGAN, we curate a dataset comprising 10,000 images with two distinct classes specifically designed for segmentation applications. To evaluate the effectiveness of DCGAN-based generation, we propose and assess a novel loss function. For accurate segmentation of the leaf disease, we utilize a UNet architecture with a custom backbone based on the MobileNetV4 CNN. The proposed segmentation model yields an average pixel accuracy of 0.91 and an mIoU (mean intersection over union) of 0.95. Furthermore, we explore different UNet-based segmentation approaches and evaluate the performance of various backbones to assess their effectiveness. By leveraging deep learning techniques, including DCGAN for dataset generation and the UNet framework for precise segmentation, we significantly contribute to the development of effective methods for detecting and segmenting leaf diseases in jasmine plants.