Vision-language model CA-CLIP diagnoses multiple coexisting leaf diseases from a single image

Deep learning models have made impressive strides in plant disease recognition, but most were trained and evaluated under the assumption that one image corresponds to one disease.

Annotating real images of multiple diseases on the same leaf is costly, and the resulting datasets are often small and imbalanced.

The team therefore built a benchmark called CC-Leaf, a resource for coexisting leaf diseases, by combining real disease images with controlled image synthesis.

Subject of Research: CA-CLIP for multi-label diagnosis of co-occurring plant leaf diseases: A class-aware vision-language framework Article Title: CA-CLIP for multi-label diagnosis of co-occurring plant leaf diseases: A class-aware vision-language framework Article References: Liu, S., Wu, J., Zhao, W., Liu, J., Chen, J., & Cui, Y.

CA-CLIP for multi-label diagnosis of co-occurring plant leaf diseases: A class-aware vision-language framework.