Multi-scale feature fusion-based vision mamba for robust plant disease image classification on field-acquired plantdoc data

Introduction: Existing convolutional neural networks and Transformers cannot effectively capture fine-grained local lesion features and long-range contextual dependencies simultaneously in field-collected plant images. To address this research limitation, we aim to design an effective lightweight model suitable for plant disease identification in complex field scenarios. Methods: This work proposes an improved Vision Mamba network for plant disease classification based on the challenging PlantDoc dataset. Three dedicated modules are embedded into the framework, including the Multi-Scale Feature Fusion Module (MFFM), Adaptive Channel Attention Mechanism (ACAM) and Lightweight Residual Connection (LRC). The MFFM fuses multi-scale texture, shape and semantic lesion features extracted from shallow, medium and deep network layers. The ACAM adaptively highlights disease-related feature channels and suppresses irrelevant background interference. The LRC structure is adopted to relieve the gradient vanishing problem existing in deep selective state space model (SSM) networks. Results: Experimental results on the filtered PlantDoc dataset show that the presented model obtains an overall accuracy of 92.67%, macro precision of 91.83%, macro recall of 91.56% and macro F1-score of 91.70% on independent test samples, which outperforms the original Vision Mamba baseline by 5.33% in accuracy. Five-fold stratified cross-validation achieves stable accuracy at 92.41 ± 0.24%, and paired t-tests prove that the performance improvement is statistically significant with p<0.05. Ablation experiments confirm the combined contribution of the three designed modules. Discussion: Error analysis and confusion matrix visualization reveal that the main classification errors are derived from high similarity among different plant disease categories. This study fully verifies the application potential of state space models in agricultural computer vision tasks. The proposed method can serve as an efficient technical scheme for intelligent identification of crop diseases and is well applicable to edge device deployment in precision agriculture practice.