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GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8

Year: 2025
Type: article
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 protected]) 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.
DOI: 10.3390/app15073910
Authors: Qiang Hu, Yunhua Zhang
Language: English
Subfield: Plant Science
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4409103415
FWCI: 5.624
Cites: 38
Cited by: 11
Related to: 10