To solve the problem of limited generalization ability that is widely existing in lightweight models used for leaf disease detection, this paper puts forward a lightweight detection model named CE-Fusion Botanic, which is based on the adaptive control of local–global information fusion. Therefore, this model includes a globally guided dynamic gating fusion mechanism that dynamically adjusts fusion weights between local features, such as spot lesions, and global semantic features, such as symptoms of systemic infection, thus realizing adaptive perception of the dual characteristics of plant diseases. Hence, the local information extraction branch combines an improved MobileNetV3-Small structure and a CBAM attention mechanism, while the global information extraction branch uses a lightweight Vision Transformer (ViT) design called EffiViT. Comprehensive contrast experiments were carried out by using seven mainstream lightweight models on the PlantVillage tomato disease subset, the full-category PlantVillage leaf disease dataset, and the Grapevine leaf disease dataset. Models were divided into large-scale, medium-scale, and small-scale groups according to the number of parameters. The results show that CE-Fusion Botanic is significantly better than comparative methods in both detection accuracy and generalization performance, and at the same time, it keeps a lightweight profile, which demonstrates superior cross-dataset adaptation capabilities.
Bao et al. (Wed,) studied this question.