An Efficient Model for Leafy Vegetable Disease Detection and Segmentation Based on Few-Shot Learning Framework and Prototype Attention Mechanism.

Abstract

This study proposes a model for leafy vegetable disease detection and segmentation based on a few-shot learning framework and a prototype attention mechanism, with the aim of addressing the challenges of complex backgrounds and few-shot problems. Experimental results show that the proposed method performs excellently in both object detection and semantic segmentation tasks. In the object detection task, the model achieves a precision of 0.93, recall of 0.90, accuracy of 0.91, mAP@50 of 0.91, and mAP@75 of 0.90. In the semantic segmentation task, the precision is 0.95, recall is 0.92, accuracy is 0.93, mAP@50 is 0.92, and mAP@75 is 0.92. These results show that the proposed method significantly outperforms the traditional methods, such as YOLOv10 and TinySegformer, validating the advantages of the prototype attention mechanism in enhancing model robustness and fine-grained feature expression. Furthermore, the prototype loss function, which optimizes the distance relationship between samples and category prototypes, significantly improves the model's ability to discriminate between categories. The proposed method shows great potential in agricultural disease detection, particularly in scenarios with few samples and complex backgrounds, offering broad application prospects.

Plant phenotyping relevance

葉菜の病害を画像から検出・セグメンテーションするモデルを開発し、既存手法と性能比較しているため、植物の病害状態を対象とするフェノタイピング手法が中心です。

Code and data availability

The supplied blocks describe a self-collected leafy vegetable disease image dataset (China Agricultural University Science Park plus online images) and a few-shot detection/segmentation model, but contain no public dataset deposit, no author code/model availability statement, and no public URL. No paper-specific public

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