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.
Fu et al. (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: