A rapid identification method for maize leaf spot disease based on the improved YOLOv8 model

doi:10.14088/j.cnki.issn0439-8114.2025.05.025

Abstract

Abstract: To achieve rapid identification of maize leaf spot disease, the detection performance of the improved YOLOv8 model was optimized by integrating the Global Attention Module (GAM), Slim-Neck lightweight module, and Inner-CIoU loss function. Compared with the original YOLOv8 model, the improved YOLOv8 model (GAM+Slim-Neck+Inner-CIoU) showed increases of 4.15% in Precision, 5.51% in Recall, 3.91% in mAP @0.5, and 11.35% in mAP @[0.5∶0.95], while the number of parameters and detection time decreased by 10.39% and 3.42%, respectively. The improved YOLOv8 model outperformed other models (YOLOv3, YOLOv5, YOLOv6, and Faster R-CNN) in Precision, Recall, mAP @0.5, and mAP @[0.5∶0.95], while also demonstrating significant advantages in parameter quantity and detection time, combining high efficiency with lightweight characteristics. The improved YOLOv8 model efficiently captured critical information, fully integrated multi-dimensional features, and rationally allocated computational resources, thereby enhancing recognition accuracy.

Key words: maize, leaf spot disease, improvement, YOLOv8 model, rapid identification

CLC Number:

ZHANG Lu, WU Xue-lian. A rapid identification method for maize leaf spot disease based on the improved YOLOv8 model[J]. HUBEI AGRICULTURAL SCIENCES, 2025, 64(5): 160-166.

References

[1] 赵玉霞. 基于图像识别的玉米叶部病害诊断技术研究[D].北京:北京邮电大学,2007.
[2] 樊湘鹏,周建平,许燕.基于改进区域卷积神经网络的田间玉米叶部病害识别[J].华南农业大学学报, 2020, 41(6):10.
[3] 慕君林,马博,王云飞,等.基于深度学习的农作物病虫害检测算法综述[J].农业机械学报, 2023, 54(S2):301-313.
[4] 边柯橙,杨海军,路永华.深度学习在农业病虫害检测识别中的应用综述[J].软件导刊, 2021, 20(3):26-33.
[5] 杨锋,姚晓通.基于改进YOLOv8的小麦叶片病虫害检测轻量化模型[J].智慧农业中英文, 2024, 6(1):147-157.
[6] 刘润飞. 基于改进YOLOv8的棉花虫害检测算法[J].农业工程, 2024, 14(7):42-47.
[7] 朱婷茹. 基于无人机影像的松林变色立木识别方法研究[D].南京:南京林业大学,2023.
[8] 高伟锋. 基于YOLOv8的柑橘病虫害识别系统研究与设计[J].智慧农业导刊, 2023, 3(15):27-30.
[9] 张书贵,陈书理,赵展.改进YOLOv8的农作物叶片病虫害识别算法[J].中国农机化学报, 2024,45(7):255-260.
[10] 李志良,李梦霞,董勇,等.基于改进YOLO v8的轻量化玉米害虫识别方法[J].江苏农业科学, 2024, 52(14):196-206.
[11] SHRIVASTAVA A, GUPTA A.Contextual priming and feedback for faster R-CNN[A].Computer vision-ECCV 2016: 14th European conference[C].Amsterdam, Netherlands:Springer, 2016.
[12] HE K, ZHANG X, REN S, et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9):1904-1916.
[13] DEVRIES T, TAYLOR G W.Improved regularization of convolutional neural networks with cutout[J].arXiv preprint arXiv:170804552. 2017.
[14] ZHANG H, CISSE M, DAUPHIN Y N,et al.Mixup: Beyond empirical risk minimization[J].arXiv preprint arXiv:171009412. 2017.
[15] HENDRYCKS D, MU N, CUBUK E D, et al.Augmix: A simple data processing method to improve robustness and uncertainty[J].arXiv preprint arXiv:191202781. 2019.
[16] ROY A M, BHADURI J.DenseSPH-YOLOv5: An automated damage detection model based on denseNet and swin-transformer prediction head-enabled YOLOv5 with attention mechanism[J].Advanced engineering informatics,2023,56:102007.
[17] ROY A M, BHADURI J, KUMAR T, et al.WilDect-YOLO: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection[J].Ecological informatics,2023,75:101919.
[18] WU D, JIANG S, ZHAO E, et al.Detection of Camellia oleifera fruit in complex scenes by using YOLOv7 and data augmentation[J].Applied sciences,2022,12(22):11318.
[19] ZHAO Q, YANG L, LYU N.A driver stress detection model via data augmentation based on deep convolutional recurrent neural network[J].Expert systems with applications,2024, 238:122056.
[20] LI F,ZHANG H,XU H,et al.Mask dino: Towards a unified transformer-based framework for object detection and segmentation[A].IEEE conference on computer vision and pattern recognition[C].IEEE,2023. 3041-3050.
[21] YE Z, GUO Q, WEI J,et al.Recognition of terminal buds of densely-planted Chinese fir seedlings using improved YOLOv5 by integrating attention mechanism[J].Frontiers in plant science,2022,13:991929.
[22] ZHENG Z, HU Y, QIAO Y, et al.Real-time detection of winter jujubes based on improved YOLOX-nano network[J].Remote sensing,2022,14(19):4833.
[23] HU J, SHEN L, SUN G.Squeeze-and-excitation networks[A].Proceedings of the IEEE conference on computer vision and pattern recognition[C].IEEE,2018. 7132-7141.