Staple crops are the cornerstone of the food supply but are frequently threatened by plant diseases. Effective disease management, including disease identification and severity assessment, helps to better address these challenges. Currently, methods for disease severity assessment typically rely on calculating the area proportion of disease segmentation regions or using classification networks for severity assessment. However, these methods require large amounts of labeled data and fail to quantify lesion proportions when using classification networks, leading to inaccurate evaluations. To address these issues, we propose an automated framework for disease severity assessment that combines multi-task learning and knowledge-driven large-model segmentation techniques. This framework includes an image information processor, a lesion and leaf segmentation module, and a disease severity assessment module. First, the image information processor utilizes a multi-task learning strategy to analyze input images comprehensively, ensuring a deep understanding of disease characteristics. Second, the lesion and leaf segmentation module employ prompt-driven large-model technology to accurately segment diseased areas and entire leaves, providing detailed visual analysis. Finally, the disease severity assessment module objectively evaluates the severity of the disease based on professional grading standards by calculating lesion area proportions. Additionally, we have developed a comprehensive database of diseased leaf images from major crops, including several task-specific datasets. Experimental results demonstrate that our framework can accurately identify and assess the types and severity of crop diseases, even without extensive labeled data. Codes and data are available at http://dkp-ads.samlab.cn/.

Segmentation of vegetation remote sensing images can minimize the interference of background, thus achieving efficient monitoring and analysis for vegetation information. The segmentation of vegetation poses a significant challenge due to the inherently complex environmental conditions. Currently, there is a growing trend of using spectral sensing combined with deep learning for field vegetation segmentation to cope with complex environments. However, two major constraints remain: the high cost of equipment required for field spectral data collection; the availability of field datasets is limited and data annotation is time-consuming and labor-intensive. To address these challenges, we propose a weakly supervised approach for field vegetation segmentation by using spectral reconstruction (SR) techniques as the foundation and drawing on the theory of vegetation index (VI). Specifically, to reduce the cost of data acquisition, we propose SRCNet and SRANet based on convolution and attention structure to reconstruct multispectral images of fields, respectively. Then, borrowing from the VI principle, we aggregate the reconstructed data to establish the connection of spectral bands, obtaining more salient vegetation information. Finally, we employ the adaptation strategy to segment the fused feature map using a weakly supervised method, which does not require manual labeling to obtain a field vegetation segmentation result. Our segmentation method can achieve a Mean Intersection over Union (MIoU) of 0.853 on real field datasets, which outperforms the existing methods. In addition, we have open-sourced a dataset of unmanned aerial vehicle (UAV) RGB-multispectral images, comprising 2358 pairs of samples, to improve the richness of remote sensing agricultural data. The code and data are available at ​https://github.com/GZU-SAMLab/VegSegment_SR, and ​http://sr-seg.samlab.cn/.