Detection of pest and disease in tea based on improved YOLOv8s

doi:10.3969/j.issn.1004-1524.20240836

Acta Agriculturae Zhejiangensis ›› 2025, Vol. 37 ›› Issue (9): 1933-1942.DOI: 10.3969/j.issn.1004-1524.20240836

• Plant Protection • Previous Articles Next Articles

Detection of pest and disease in tea based on improved YOLOv8s

LIU Rui¹(), WANG Lijuan²^,^*(), WANG Qiuhao¹, LIN Xudong¹, GUO Qihang¹, XU Duolin¹, LI Wenyan¹

¹ College of International Education, Guangdong University of Technology, Guangzhou 510006, China
² The School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China

Received:2024-09-13 Online:2025-09-25 Published:2025-10-15
Contact: WANG Lijuan

Abstract

Abstract:

The detection of pests and diseases in tea cultivation mainly relies on traditional manual methods, which has problems of low efficiency and poor precision. This paper proposed an improved YOLOv8s model, which aimed to achieve real-time and efficient detection of pests and diseases in tea. A dataset containing five common pests and diseases was constructed in this model. By adding the convolutional block attention module (CBAM), the representation ability of model was enhanced, important feature extraction was enhanced, and the recognition precision of model was improved. The FasterNet backbone network was adopted to reduce detection time while maintaining high precision and significantly reduce the model complexity. By adopting the WIoU loss function, the object overlapping problem in the process of image processing was effectively solved and the detection performance of model in real situations was improved. The results showed that compared with the traditional YOLOv8s model, the mean average precision of improved YOLOv8s-CFW model reached 98.2%, which was improved by 3.7 percentage points, the floating-point operations per second (FLOPS) and parameter count were reduced by 43.0% and 45.2%, respectively, and the detection time was reduced by 34.8%.The improved model demonstrates significant improvements in both precision and lightweight, meets the requirements of real-time detection, and provides reliable theoretical support for the automated detection of pests and diseases in tea.

Key words: tea, YOLOv8s, detection of pest and disease, lightweight

CLC Number:

S571.1

LIU Rui, WANG Lijuan, WANG Qiuhao, LIN Xudong, GUO Qihang, XU Duolin, LI Wenyan. Detection of pest and disease in tea based on improved YOLOv8s[J]. Acta Agriculturae Zhejiangensis, 2025, 37(9): 1933-1942.

Figures/Tables 10

Fig.1 Lesion phenotype of pests and diseases in tea

Fig.2 Structure diagram of model before improvement Detect, Detection head; Concat, Stitching operation of feature map; Upsample, Upsampling operation; C2f, Feature extraction module; Conv, Convolution operation; SPPF, Spatial pyramid pooling-fast layer.

Fig.3 Schematic of CBAM

Fig.4 Structure of FasterNet h,Height of the input feature map;w,Width of the input feature map;c1-c4,Number of channels output in stages 1 to 4;l1-l4,Number of blocks stacked in stages 1 to 4.

Fig.5 Schematic of IoU

Fig.6 Structure diagram of model after improvement Backbone,Backbone network;Neck,Feature fusion layer;Head,Output detection head;Concat,Stitching operation of feature map; Upsample, Upsampling operation;C2f,Feature extraction module;Conv,Convolution operation;SPPF,Spatial pyramid pooling-fast layer.

Table 1 Comparison of ablation experiment results

模型 Model	mAP/%	Params/M	FLOPS/10⁹	检测时间 Detection time/ms	模型大小 Model size/MB
YOLOv8s	94.4	11.13	28.4	4.6	22.5
YOLOv8s-CBAM	96.2	11.39	28.7	4.5	23.1
YOLOv8s-FasterNet	96.5	6.08	16.1	2.9	12.4
YOLOv8s-WIoU	95.4	11.13	28.4	4.5	22.5
YOLOv8s-CBAM-FasterNet	96.8	6.10	16.2	3.0	12.4
YOLOv8s-FasterNet-WIoU	96.5	6.08	16.1	3.0	12.4
YOLOv8s-CBAM-WIoU	96.8	11.39	28.7	4.5	23.1
YOLOv8s-CFW	98.2	6.10	16.2	3.0	12.4

Table 2 Comparison of detection precision of pests and diseases in tea before and after improving the model %

模型 Model	平均精度Average precision						平均精度均值 mAP
模型 Model	藻斑病 Algal spot	茶轮斑病 Brown blight	云纹叶枯病 Gray blight	健康叶片 Healthy leaf	茶角盲蝽病 Helopeltis	红斑病 Red spot	平均精度均值 mAP
YOLOv8s	95.5	93.7	95.8	95.1	95.2	91.4	94.5
YOLOv8s-CFW	99.1	97.4	99.5	98.9	98.9	95.4	98.2

Fig.7 Detection effect of pests and diseases in tea before and after improving the model The numbers in the figure represent confidence levels.

Table 3 Parameters of different models

模型 Model	mAP/%	Params/10⁶	FLOPS/10⁹	检测时间 Detection time/ms	模型大小 Model size/MB
YOLOV3-tiny	94.0	8.68	12.9	2.7	17.4
YOLOv5s	94.2	7.03	15.8	5.9	14.5
YOLOv7	92.9	36.51	103.2	12.5	74.8
YOLOv8s	94.5	11.13	28.4	4.6	22.5
YOLOv9s	93.7	9.60	38.7	4.7	20.3
YOLOv8s-CFW	98.2	6.10	16.2	3.0	12.4

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Detection of pest and disease in tea based on improved YOLOv8s

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