ProtoLeafNet: A Prototype Attention-Based Leafy Vegetable Disease Detection and Segmentation Network for Sustainable Agriculture

In response to the challenges posed by visually similar disease symptoms, complex background noise, and the need for fine-grained disease classification in leafy vegetables, this study proposes ProtoLeafNet—a prototype attention-based deep learning model for multi-task disease detection and segmentation. By integrating a class-prototype–guided attention mechanism with a prototype loss function, the model effectively enhances the focus on lesion areas and improves category discrimination. The architecture leverages a dual-task framework that combines object detection and semantic segmentation, achieving robust performance in real agricultural scenarios. Experimental results demonstrate that the model attains a detection precision of 93.12%, recall of 90.27%, accuracy of 91.45%, and mAP scores of 91.07% and 90.25% at IoU thresholds of 50% and 75%, respectively. In the segmentation task, the model achieves a precision of 91.79%, recall of 90.80%, accuracy of 93.77%, and mAP@50 and mAP@75 both reaching 90.80%. Comparative evaluations against state-of-the-art models such as YOLOv10 and TinySegformer verify the superior detection accuracy and fine-grained segmentation ability of ProtoLeafNet. These results highlight the potential of prototype attention mechanisms in enhancing model robustness, offering practical value for intelligent disease monitoring and sustainable agriculture.