Soybean Leaf Disease Recognition Based on Sem-ResFormer and Multimodal Large Models

In response to the challenges of insufficient multi-scale feature representation and limited model adaptability in soybean leaf disease recognition from field images, a semantic residual Transformer (Sem-ResFormer) model is proposed for soybean leaf disease identification. The proposed model is constructed by integrating multi-scale residual feature extraction, Transformer-based global dependency modeling, and a semantic mapping mechanism, through which effective modeling and semantic representation of multi-scale visual information in lesion regions are achieved. A multimodal large model fine-tuning strategy combined with cross-architecture hyperparameter transfer is employed. The optimal hyperparameter configuration of the Vision Transformer, obtained via Bayesian optimization, is transferred to Qwen2.5-VL, and a progressive fine-tuning strategy is adopted, whereby the adaptability of the model to task-specific data is gradually enhanced. Experiments were conducted on a constructed five-class field-image soybean leaf disease dataset containing 3852 images, with 674 labeled images used in the initial few-shot fine-tuning stage. Under an input resolution of 720 × 720, the proposed method achieved an overall accuracy (OA) of 95.33%, surpassing the OA obtained with the default parameter configuration (93.64%) and the ResNet-50-based transfer method (93.43%). In the initial few-shot stage, the OA was improved from 74.05% under zero-shot conditions to 90.66%. These results demonstrate that the proposed method effectively improves soybean leaf disease recognition accuracy and model adaptability under the constructed field-image dataset with visual variability.