SegPPD-FS: Segmenting plant pests and diseases in the wild using few-shot learning

Accurate segmentation of areas affected by pests and diseases is essential for precisely assessing the severity and spread of infections, thereby facilitating the development of effective management and intervention strategies. Obtaining high-quality pixel-level annotations for training deep learning models in agricultural environments poses considerable challenges. To overcome this limitation, the present work introduces a novel semantic segmentation approach (SegPPD-FS) that employs few-shot learning techniques to reduce annotation demands while effectively segmenting plant pests and diseases. The proposed SegPPD-FS comprises two key components: the similarity feature enhancement module (SFEM) and the hierarchical prior knowledge injection module (HPKIM). The SFEM refines foreground targets by employing a lightweight attention mechanism to mitigate irrelevant background interference in natural images and further enhances the discriminative capability of query features. The HPKIM is designed to address the difficulties associated with identifying pests and diseases that vary widely in terms of shape and size within field images, which is achieved through a hierarchical integration of multiscale contextual data into the query feature representations. In addition, this study constructed and publicly released a high-quality few-shot semantic segmentation (FSS) dataset that included 101 distinct categories of plant pests and diseases, which supports further research on the precise monitoring of plant health issues. The experimental results demonstrate that the proposed method achieves mIoU values of 71.19 ​% and 71.58 ​% with the 1-shot and 2-shot settings, respectively, on the released dataset. This performance surpasses that of other FSS techniques, such as SegGPT and PerSAM, providing a promising and label-efficient solution for pest and disease monitoring. The collected dataset, which focuses on plant pests and diseases, has been publicly released at https://doi.org/10.5281/zenodo.15114159, providing a valuable resource for evaluating various FSS techniques.