GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8
Source abstract
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 (mAP@0.5) 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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Spectroscopy and Chemometric Analyses · Advanced Chemical Sensor Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
References (41)
Show source-provided references
1. Alston, J.M., and Sambucci, O. (2019). Grapes in the world economy. The Grape Genome, Springer.10.1007/978-3-030-18601-2_1 ↗
2. (2024, October 16). Food and Agriculture Organization of the United Nations. Available online: https://www.fao.org/faostat/zh/#data/QCL.
3. Khan, N., Fahad, S., Naushad, M., and Faisal, S. (2020). Grape production critical review in the world. SSRN, 3595842.10.2139/ssrn.3595842 ↗
4. What if esca disease of grapevine were not a fungal disease?10.1007/s13225-012-0171-z ↗
5. An overview of the research on plant leaves disease detection using image processing techniques10.9790/0661-16151016 ↗
6. Predicting the spread of postharvest disease in stored fruit, with application to apples10.1016/j.postharvbio.2013.04.003 ↗
7. Boulent, J., Beaulieu, M., St-Charles, P.L., Théau, J., and Foucher, S. (2019). Deep learning for in-field image-based grapevine downy mildew identification. Precision Agriculture’19, Wageningen Academic.10.3920/978-90-8686-888-9_16 ↗
8. Research progress of deep learning in detection and recognition of plant leaf diseases
9. Xiong, J., Yu, D., Liu, S., Shu, L., Wang, X., and Liu, Z. (2021). A review of plant phenotypic image recognition technology based on deep learning. Electronics, 10.10.3390/electronics10010081 ↗
10. Padol, P.B., and Yadav, A.A. (2016, January 9–11). SVM classifier based grape leaf disease detection. Proceedings of the 2016 Conference on Advances in Signal Processing (CASP), Pune, India.10.1109/CASP.2016.7746160 ↗
11. Image-based detection of plant diseases: From classical machine learning to deep learning journey10.1155/2021/5541859 ↗
12. Xue, Z., Xu, R., Bai, D., and Lin, H. (2023). YOLO-tea: A tea disease detection model improved by YOLOv5. Forests, 14.10.3390/f14020415 ↗
13. End-to-end deep learning model for corn leaf disease classification10.1109/ACCESS.2022.3159678 ↗
14. Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv.
15. Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv.
16. (2025, March 20). Ultralytics. Available online: https://github.com/ultralytics/ultralytics.
17. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11–14). Ssd: Single shot multibox detector. Proceedings of the Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Part I 14.10.1007/978-3-319-46448-0_2 ↗
18. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21–26). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
19. He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017, January 22–29). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.322 ↗
20. Zhu, R., Zou, H., Li, Z., and Ni, R. (2022). Apple-Net: A model based on improved YOLOv5 to detect the apple leaf diseases. Plants, 12.10.3390/plants12010169 ↗
21. RiceNet: A two stage machine learning method for rice disease identification10.1016/j.biosystemseng.2022.11.007 ↗
22. Attention embedded lightweight network for maize disease recognition10.1111/ppa.13322 ↗
23. Cucumber leaf disease identification with global pooling dilated convolutional neural network10.1016/j.compag.2019.03.012 ↗
24. Bi, C., Xu, S., Hu, N., Zhang, S., Zhu, Z., and Yu, H. (2023). Identification method of corn leaf disease based on improved Mobilenetv3 model. Agronomy, 13.10.3390/agronomy13020300 ↗
25. Ensemble model for grape leaf disease detection using CNN feature extractors and random forest classifier10.1016/j.heliyon.2024.e33377 ↗
26. A hybrid model of ghost-convolution enlightened transformer for effective diagnosis of grape leaf disease and pest10.1016/j.jksuci.2022.03.006 ↗
27. Liu, B., Ding, Z., Tian, L., He, D., Li, S., and Wang, H. (2020). Grape leaf disease identification using improved deep convolutional neural networks. Front. Plant Sci., 11.10.3389/fpls.2020.01082 ↗
28. Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 13–19). Ghostnet: More features from cheap operations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.00165 ↗
29. Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., and Sun, J. (2021, January 19–25). Repvgg: Making vgg-style convnets great again. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.10.1109/CVPR46437.2021.01352 ↗
30. Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8–14). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_1 ↗
31. Lyu, C., Zhang, W., Huang, H., Zhou, Y., Wang, Y., Liu, Y., Zhang, S., and Chen, K. (2022). Rtmdet: An empirical study of designing real-time object detectors. arXiv.
32. Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 17–24). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPR52729.2023.00721 ↗
33. Ma, X., Dai, X., Bai, Y., Wang, Y., and Fu, Y. (2024, January 16–22). Rewrite the stars. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR52733.2024.00544 ↗
34. Hu, J., Shen, L., and Sun, G. (2018, January 18–22). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00745 ↗
35. Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 14–19). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.01155 ↗
36. Ouyang, D., He, S., Zhang, G., Luo, M., Guo, H., Zhan, J., and Huang, Z. (2023, January 4–10). Efficient multi-scale attention module with cross-spatial learning. Proceedings of the ICASSP 2023–2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.10.1109/ICASSP49357.2023.10096516 ↗
37. Shang, J., Wang, J., Liu, S., Wang, C., and Zheng, B. (2023). Small target detection algorithm for UAV aerial photography based on improved YOLOv5s. Electronics, 12.10.3390/electronics12112434 ↗
38. Pruning and quantization for deep neural network acceleration: A survey10.1016/j.neucom.2021.07.045 ↗
39. Channel pruned YOLO V5s-based deep learning approach for rapid and accurate apple fruitlet detection before fruit thinning10.1016/j.biosystemseng.2021.08.015 ↗
40. KDnet-RUL: A knowledge distillation framework to compress deep neural networks for machine remaining useful life prediction10.1109/TIE.2021.3057030 ↗
41. Ssd-kd: A self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images10.1016/j.media.2022.102693 ↗