摘要Although plant disease recognition is highly important in agricultural production,traditional methods face challenges due to the high costs associated with data collection and the scarcity of samples.Few-shot plant disease identification tasks,which are based on transfer learning,can learn feature representations from a small amount of data;however,most of these methods require pretraining within the relevant domain.Recently,foundation models have demonstrated excellent performance in zero-shot and few-shot learning scenarios.In this study,we explore the potential of foundation models in plant disease recognition by proposing an efficient few-shot plant disease recognition model(PlantCaFo)based on foundation models.This model operates on an end-to-end network structure,integrating prior knowledge from multiple pretraining models.Specifically,we design a lightweight dilated contextual adapter(DCon-Adapter)to learn new knowledge from training data and use a weight decomposition matrix(WDM)to update the text weights.We test the proposed model on a public dataset,PlantVillage,and show that the model achieves an accuracy of 93.53％in a"38-way 16-shot"setting.In addition,we conduct experiments on images collected from natural environments(Cassava dataset),achieving an accuracy improvement of 6.80％over the baseline.To validate the model's generalization performance,we prepare an out-of-distribution dataset with 21 categories,and our model notably increases the accuracy of this dataset.Extensive experiments demonstrate that our model exhibits superior performance over other models in few-shot plant disease identification.