A dual-branch model combining convolution and vision transformer for crop disease classification.

ABSTRACT: Computer vision holds tremendous potential in crop disease classification, but the complex texture and shape characteristics of crop diseases make disease classification challenging. To address these issues, this paper proposes a dual-branch model for crop disease classification, which combines Convolutional Neural Network (CNN) with Vision Transformer (ViT). Here, the convolutional branch is utilized to capture the local features while the Transformer branch is utilized to handle global features. A learnable parameter is used to achieve a linear weighted fusion of these two types of features. An Aggregated Local Perceptive Feed Forward Layer (ALP-FFN) is introduced to enhance the model's representation capability by introducing locality into the Transformer encoder. Furthermore, this pape

SUBMITTER: Meng Q 

PROVIDER: S-EPMC12021192 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature