A lightweight MHDI-DETR model for detecting grape leaf diseases.

ABSTRACT: Accurate diagnosis of grape leaf diseases is critical in agricultural production, yet existing detection techniques face challenges in achieving model lightweighting while ensuring high accuracy. In this study, a real-time, end-to-end, lightweight grape leaf disease detection model, MHDI-DETR, based on an improved RT-DETR architecture, is presented to address these challenges. The original residual backbone network was improved using the MobileNetv4 network, significantly reducing the model's computational requirements and complexity. Additionally, a lightSFPN feature fusion structure is presented, combining the Hierarchical Scale Feature Pyramid Network with the Dilated Reparam Block structure design from the UniRepLKNet network. This structure is designed to overcome the challenges of ca

SUBMITTER: Fu Z 

PROVIDER: S-EPMC11659005 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature