Accurate identification of plant diseases is paramount for ensuring optimal crop health and minimizing yield losses. This study focuses on Solanum melongena L. (eggplant), a widely cultivated vegetable highly susceptible to fungal infections. We employ a semantic segmentation approach to detect leaf symptoms and damage, utilizing deep learning models. A comparative analysis is conducted on three convolutional neural network architectures: U2-Net, U-Net, and WU-Net, for image segmentation tasks. Each model is trained on a dataset comprising 500 augmented images derived from an initial set of 200 images, with a resolution of 256 × 256 × 3. Model performance is evaluated based on pixel accuracy, Dice coefficient, and Intersection over Union. Experimental results demonstrate that U2-Net exhibits outstanding performance, particularly in capturing fine-grained details, attributed to its deeper architecture and enhanced feature extraction capabilities. The proposed U2-Net model achieved a training accuracy of 96% and test accuracy of 93%, demonstrating its effectiveness in precise symptom detection and plant disease classification. This study contributes to leaf disease detection, facilitating timely intervention and targeted disease management in agriculture.
Patil et al. (Fri,) studied this question.