Efficient and Lightweight LD-SAGE Model for High-Accuracy Leaf Disease Segmentation in Understory Ginseng

Understory ginseng, with superior quality compared to field-cultivated varieties, is highly susceptible to diseases, which negatively impact both its yield and quality. Therefore, this paper proposes a lightweight, high-precision leaf spot segmentation model, Lightweight DeepLabv3+ with a StarNet Backbone and Attention-guided Gaussian Edge Enhancement (LD-SAGE). This study first introduces StarNet into the DeepLabv3+ framework to replace the Xception backbone, reducing the parameter count and computational complexity. Secondly, the Gaussian-Edge Channel Fusion module uses multi-scale Gaussian convolutions to smooth blurry areas, combining Scharr edge-enhanced features with a lightweight channel attention mechanism for efficient edge and semantic feature integration. Finally, the proposed Multi-scale Attention-guided Context Modulation module replaces the traditional Atrous Spatial Pyramid Pooling. It integrates Multi-scale Grouped Dilated Convolution, Convolutional Multi-Head Self-Attention, and dynamic modulation fusion. This reduces computational costs and improves the model’s ability to capture contextual information and texture details in disease areas. Experimental results show that the LD-SAGE model achieves an mIoU of 92.48%, outperforming other models in terms of precision and recall. The model’s parameter count is only 4.6% of the original, with GFLOPs reduced to 22.1% of the baseline model. Practical deployment experiments on the Jetson Orin Nano device further confirm the advantage of the proposed method in the real-time frame rate, providing support for the diagnosis of leaf diseases in understory ginseng.