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.