MoSViT: a lightweight vision transformer framework for efficient disease detection via precision attention mechanism.

ABSTRACT: Maize, a globally essential staple crop, suffers significant yield losses due to diseases. Traditional diagnostic methods are often inefficient and subjective, posing challenges for timely and accurate pest management. This study introduces MoSViT, an innovative classification model leveraging advanced machine learning and computer vision technologies. Built on the MobileViT V2 framework, MoSViT integrates the CLA focus mechanism, DRB module, MoSViT Block, and the LeakyRelu6 activation function to enhance feature extraction accuracy while reducing computational complexity. Trained on a dataset of 3,850 images encompassing Blight, Common Rust, Gray Leaf Spot, and Healthy conditions, MoSViT achieves exceptional performance, with classification accuracy, Precision, Recall, and F1 Score of 98.

SUBMITTER: Chen Y 

PROVIDER: S-EPMC11979205 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature