# GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8
###### Abstract  
In view of the issues of high complexity, significant computational resource consumption, and slow inference speed in the detection algorithm for grape leaf diseases, this paper proposes GCS-YOLO, a lightweight detection algorithm based on an improved YOLOv8. The lightweight feature extraction module C2f-GR is proposed to replace the C2f module. C2f-GR achieves lightweight design while effectively capturing detailed features of multi-scale information by replacing partial convolutions in C2f with Ghost Modules. Additionally, RepConv is incorporated into C2f-GR to avoid the complexity of multi-branch structures and enhance gradient flow capability. The CBAM attention mechanism is added to the model to improve the extraction of subtle features of lesions in complex environments. Cross-scale shared convolution parameters and separated batch normalization techniques are used to optimize the detection head, achieving a lightweight design and improving the detection efficiency of the algorithm. Experimental results indicate that the improved model has a number of parameters and computational load of 1.63 M and 4.5 G, respectively, with a mean average precision ([EMAIL_ADDRESS]) of 96.2% and a model size of only 3.5 MB. The number of parameters and computational load of the improved model have been reduced by 45.7% and 45.1%, respectively, compared to the baseline model, while the mAP has increased by 1.3%. This lightweight design not only ensures detection accuracy to meet the real-time detection needs of grape leaf diseases but is also more suitable for edge deployment, demonstrating broad application prospects. YOLOv8; grape leaf disease; lightweight; GhostNet; RepConv; CBAM attention mechanism +
Footnote †: journal: Applied sciences  
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diseases on deployed devices could provide timely guidance for agricultural production, ultimately improving grape yield and quality. In recent years, researchers have increasingly adopted computer vision and machine learning technologies to address disease identification challenges [8; 9; 10]. While these methods enable precise disease detection, they still require manual feature extraction, leading to inefficiency, weak generalization capabilities, and susceptibility to interference, thus limiting practical applications [11]. Conversely, deep learning methods have gained prominence in agricultural disease identification due to their exceptional automatic feature extraction capabilities and superior generalization performance in image processing tasks [12; 13]. Representative models include one-stage algorithms, such as the YOLO series (YOLOv4 [14], YOLOv8 [15], etc. ), SSD [17], and two-stage algorithms, including FPN [18] and Mask R-CNN [19]. Building on the strengths of these deep learning frameworks, researchers have further optimized architectures to tackle persistent challenges in agricultural disease detection. Specifically, [PERSON] et al. [20] proposed an enhanced YOLOv5-based model to address the limitations of existing apple leaf disease detection models, which often overlook disease diversity and recognition accuracy. By incorporating a Feature Enrichment Module (FEM) and Coordinate Attention for Efficient Mobile Network Design (CA), the improved model achieved superior performance in identifying five common diseases. Experimental results demonstrated that the proposed method outperformed the baseline YOLOv5, with the mAP increasing by 6.1 percentage points. [PERSON] et al. [21] proposed a two-stage recognition model combining YOLOX for lesion detection with a Siamese network for image recognition, achieving a mAP of 95.58% for rice disease identification under few-shot and complex background conditions. [PERSON] et al. [22] improved DenseNet by integrating depthwise separable convolution in dense blocks and embedding attention modules, attaining 95.86% mAP on a self-constructed corn dataset. [PERSON] et al. [23] proposed a novel plant disease recognition model, GPDCNN, by modifying the AlexNet architecture.