Soybean Leaf Disease Recognition Methods Based on Hyperparameter Transfer and Progressive Fine-Tuning of Large Models

Early recognition of crop diseases is essential for ensuring agricultural security and improving yield. However, traditional CNN-based methods often suffer from limited generalization when training data are scarce or when applied to transfer scenarios. To address these challenges, this study adopts the multimodal large model Qwen2.5-VL as the core and targets three major soybean leaf diseases along with healthy samples. We propose a parameter-efficient adaptation framework that integrates cross-architecture hyperparameter transfer and progressive fine-tuning. The framework utilizes a Vision Transformer (ViT) as an auxiliary model, where Bayesian optimization is applied to obtain optimal hyperparameters that are subsequently transferred to Qwen2.5-VL. Combined with existing low-rank adaptation (LoRA) and a multi-stage training strategy, the framework achieves efficient convergence and robust generalization with limited data. To systematically evaluate the model’s multi-scale visual adaptability, experiments were conducted using low-resolution, medium-resolution, and high-resolution inputs. The results demonstrate that Qwen2.5-VL achieves an average zero-shot accuracy of 71.72%. With the proposed cross-architecture hyperparameter transfer and parameter-efficient tuning strategy, accuracy improves to 88.72%, and further increases to 93.82% when progressive fine-tuning is applied. The model also maintains an accuracy of 91.0% under cross-resolution evaluation. Overall, the proposed method exhibits strong performance in recognition accuracy, feature discriminability, and multi-scale robustness, providing an effective reference for adapting multimodal large language models to plant disease identification tasks.