Enhanced Leaf Disease Segmentation Using U-Net Architecture for Precision Agriculture: A Deep Learning Approach

This study presents a deep learning-based image segmentation approach for leaf disease identification using the U-Net architecture. Convolutional neural networks (CNNs), particularly U-Net, are effective for precise segmentation tasks and were trained and validated on a high-quality "Leaf Disease Segmentation" dataset. Each image contains annotated regions of unhealthy leaf tissue, enabling the model to distinguish between healthy and infected areas. Image preprocessing and augmentation further enhanced model performance and robustness. The U-Net model, composed of an encoder for context extraction and a decoder for precise segmentation was trained to accurately identify diseased regions at the pixel level. Regularization techniques such as dropout, batch normalization, and ReLU activation were used to prevent overfitting and improve learning. Furthermore, Adam optimizer was employed with a learning rate of 0.001. The model demonstrated strong generalization by accurately segmenting disease regions in unseen validation images. It effectively captured complex patterns in both healthy and diseased leaf sections, outperforming traditional image processing techniques. Trained on 7056 images for 40 epochs, the model achieved 99.70% training accuracy, 0.062 training loss, and 98.99% validation accuracy. These results highlight the model's high accuracy, efficient learning, and robustness, making it suitable for real-world applications in precision agriculture.