Detection of Wheat Pest and Disease in Complex Backgrounds Based on Improved YOLOv8 Model

Detecting wheat diseases and pests, particularly those characterized by small targets amidst complex background interference, presents a significant challenge in agricultural re-search. To address this issue and achieve precise and efficient detection, we propose an enhanced version of YOLOv8, termed MGT-YOLO, which incorporates multi-scale edge enhancement and visual remote dependency mechanisms. Our methodology begins with the creation of a comprehensive dataset, WheatData, comprising 2393 high-resolution images capturing various wheat diseases and pests across different growth stages in diverse agricultural settings. To improve the detection of small targets, we implemented a multi-scale edge amplification technique within the backbone network of YOLOv8, enhancing its ability to capture minute details of wheat diseases and pests. Furthermore, we introduced the C2f GlobalContext module in the neck network, which integrates global contextual relationships and facilitates the fusion of features from small-sized objects by leveraging remote dependencies in visual imagery. Additionally, we incorporated a Vision Transformer module into the neck network to enhance the processing efficiency of small-scale disease and pest features. The proposed MGT-YOLO network was rigorously evaluated on the WheatData dataset. The results demonstrated significant improvements, with [email protected] values of 90.0% for powdery mildew and 65.5% for smut disease, surpassing the baseline YOLOv8 by 5.3% and 6.8%, respectively. The overall [email protected] reached 89.5%, representing a 2.0% improvement over YOLOv8 and outperforming other state-of-the-art detection methods. These findings suggest that MGT-YOLO is a promising solution for real-time detection of agricultural diseases and pests, offering enhanced accuracy and efficiency in complex agricultural environments.