# YOLOv8-GO: A Lightweight Model for Prompt Detection of Foliar Maize Diseases
## 1 Introduction
For instance, in 2022, [PERSON] and colleagues introduced a coordinate attention (CA) module into YOLOv5s and added a skip connection structure in the SPP (Spatial Pyramid Pooling) layer to improve both training speed and recognition accuracy [10]. In 2023, [PERSON] and his team developed the CEMLB-YOLO model for detecting corn leaf spots. Their approach integrated the CIPAM (Crucial Information Position Attention Mechanism) into YOLOv5, replaced the original Backbone network with the MobileBit (Mobile Bi-Level Transformer), and added the FRAFM (Feature Restructuring and Fusion Module) to the Neck. This model achieved an [EMAIL_ADDRESS] of 87.5% with a computational complexity of 9.4 GFLOPs [11]. Also in 2023, [PERSON] and colleagues proposed an improved model called CTR_YOLOv5n, based on YOLOv5n. This model combined the coordinate attention (CA) mechanism with a Swin Transformer detection Head, resulting in better performance in identifying corn leaf spots, Gray Spots, and Rust, thereby improving the overall recognition accuracy [12]. In the same year, [PERSON] and his team developed Corn-YOLO by adding the CSPResNeXt-50 module and the VoVGSCSP module to YOLOv7, enabling faster and more accurate detection of corn pests [13]. After the release of YOLOv8, many researchers have explored its application in plant disease detection and have achieved excellent results [14; 15; 16; 17; 18; 19]. In 2023, [PERSON] et al. compared YOLOv3 tiny, YOLOv4, YOLOv5s, YOLOv7s, and YOLOv8n, using YOLOv8n to develop a smartphone-based application for detecting and classifying corn leaf diseases. This application successfully identifies diseases through mobile phones [20]. In 2024, [PERSON] et al. replaced the original Backbone network of YOLOv8s with the GhostNet structure, introduced the Triplet Attention mechanism into the Neck structure, and substituted the CIoU_Loss function with ECIoU_Loss. Their final model achieved a mean Average Precision (mAP)@0.5 of 91.4%, with a Precision of 87.5% and a Recall of 87.7% [21]. In the same year, [PERSON] et al. enhanced YOLOv8 by incorporating a Global AttentionMechanism (GAM), a Slim-neck Module, and an Inner-CIoU Loss, significantly improving the accuracy of detecting corn leaf spot diseases [22]. When using a target detection algorithm, both positioning accuracy and classification accuracy must be considered. Therefore, the overall accuracy is not as high as that of image classification algorithms, but it can simultaneously locate multiple targets in images and videos, and can handle diverse processing, making it more suitable for dynamic multi-target scenarios in the field. In addition, the emergence of edge computing technology has provided conditions for maize disease detection to move out of the laboratory [23], but running the model on these low-performance devices requires the model to be lightweight enough. Therefore, it is necessary to develop a model that takes into account both high accuracy and light weight. Based on YOLOv8, this paper introduces the Global Attention Mechanism (GAM) to enhance multi-scale feature extraction capabilities. Additionally, the ODConv-C2f module, designed using Omni-dimensional Dynamic Convolution, is integrated to improve feature fusion quality. These enhancements aim to achieve a model that is both accurate and lightweight, ensuring better classification and localization performance.