MF-cache: CLIP-Based Multimodal Cache Model for Maize Disease Recognition

Maize is a vital economic crop that is widely used in industries, animal husbandry, and grain-oil processing. Timely identification of maize diseases is crucial for ensuring a stable yield. Currently, deep learning methods such as Convolutional Neural Networks (CNNs) have been widely applied to disease recognition. However, most existing methods rely solely on image information, overlooking the features of other modalities. Moreover, their large parameter sizes and high deployment costs hinder their practical applications. To address these challenges, we propose a lightweight image-text multimodal cache model, MF-cache, that contains only 61 000 parameters, ensuring both low computational cost and high recognition accuracy. The model leverages the multimodal pre-trained model CLIP to extract image and text features, which are fused in parallel to form a key-value cache structure enriched with domain knowledge. Additionally, a weighted two-stage fusion mechanism is introduced to dynamically adjust the contribution of each modality to the classification outcome, thereby enhancing both stability and interpretability. To improve robustness, various data augmentation strategies have been employed to increase sample diversity and mitigate overfitting in low-data scenarios. Experimental results on a self-constructed dataset, CornI&T, and the public PlantVillage dataset demonstrate the effectiveness of the proposed method, achieving 99.72% and 98.80% accuracy, respectively. These results indicate that the method achieves an excellent recognition performance while maintaining a low computational overhead, thus offering an efficient and practical solution for crop disease detection. Furthermore, it highlights the potential of combining multimodal pretrained models with few-shot learning in intelligent agricultural applications.