# GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8
###### Abstract
Comprehensive experiments conducted on a cucumber disease dataset demonstrated that the enhanced model effectively identifies cucumber diseases, achieving superior performance in accuracy and robustness compared to conventional methods. [PERSON] et al. [24] enhanced the MobileNetv3 model by integrating an Efficient Channel Attention (ECA) module, incorporating cross-layer connections between Mobile modules, and introducing dilated convolutions to expand the receptive field. Experimental results on their hybrid open-source Corn Leaf Disease Dataset (CLDD) demonstrated that the optimized model achieved significantly higher accuracy compared to other classical deep learning models. [PERSON] et al. [25] proposed a CNN-RF ensemble model with bidirectional feature extraction, attaining 95.34% mAP for grape disease detection. [PERSON] et al. [26] employed GhostNet for convolutional processing, generating feature maps through linear operations, and integrated a multi-head self-attention mechanism with a Transformer encoder, achieving an accuracy of 98.14% on grape leaf disease datasets. [PERSON] et al. [27] developed an efficient grape leaf pest detection network combining Inception structures, dense connections, and depthwise separable convolutions, reaching 97.22% mAP. These studies demonstrate significant progress in deep learning-based agricultural disease identification, yet challenges persist. Most models exhibit excessive parameter sizes and computational demands with relatively slow inference speeds, limiting deployment on edge devices. YOLOv8 maintains the high-efficiency characteristic of the YOLO series while achieving breakthroughs in accuracy and multi-task capabilities. However, it also introduces increased computational complexity and hardware deployment challenges. To address the aforementioned limitations, this study proposes the GCS-YOLO algorithm based on the YOLOv8 algorithm. In this study, the primary objective is to develop a lightweight yet accurate real-time grape leaf disease detection model by introducing an efficient network architecture and optimization strategies, thereby reducing computational resource consumption without compromising performance. Specifically, first, the Ghost Module [28] and RepConv [29] are integrated into the model and merged with the C2f module to create the C2f-GR module, which expands the receptive field while reducing the number of parameters. Second, a Convolutional Block Attention Module (CBAM) [30] is introduced to enhance perception under complex backgrounds and uneven illumination by adaptively weighting channel-wise, spatial features, and color information, thereby emphasizing the morphology of leaf disease lesions. Finally, a detection head with cross-scale shared convolutional parameters [31] is adopted to further minimize the model size, while different batch normalization (BN) layer parameters are retained to preserve model accuracy. The GCS-YOLO model achieves superior inference speed and detection accuracy, effectively fulfilling real-time grape leaf disease detection requirements. However, the current evaluation is primarily based on controlled datasets, and further field testing may be required to assess its robustness in diverse agricultural environments.