Microscopic Insect Pest Detection in Tea Plantations: Improved YOLOv8 Model Based on Deep Learning

Zejun Wang, Shihao Zhang, Lijiao Chen, Wendou Wu, Houqiao Wang, Xiaohui Liu, Zongpei Fan and Baijuan Wang ()
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Zejun Wang: College of Tea Science, Yunnan Agricultural University, Kunming 650201, China
Shihao Zhang: Yunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, China
Lijiao Chen: College of Tea Science, Yunnan Agricultural University, Kunming 650201, China
Wendou Wu: Yunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, China
Houqiao Wang: College of Tea Science, Yunnan Agricultural University, Kunming 650201, China
Xiaohui Liu: College of Tea Science, Yunnan Agricultural University, Kunming 650201, China
Zongpei Fan: College of Tea Science, Yunnan Agricultural University, Kunming 650201, China
Baijuan Wang: College of Tea Science, Yunnan Agricultural University, Kunming 650201, China

Agriculture, 2024, vol. 14, issue 10, 1-21

Abstract: Pest infestations in tea gardens are one of the common issues encountered during tea cultivation. This study introduces an improved YOLOv8 network model for the detection of tea pests to facilitate the rapid and accurate identification of early-stage micro-pests, addressing challenges such as small datasets and the difficulty of extracting phenotypic features of target pests in tea pest detection. Based on the original YOLOv8 network framework, this study adopts the SIoU optimized loss function to enhance the model’s learning ability for pest samples. AKConv is introduced to replace certain network structures, enhancing feature extraction capabilities and reducing the number of model parameters. Vision Transformer with Bi-Level Routing Attention is embedded to provide the model with a more flexible computation allocation and improve its ability to capture target position information. Experimental results show that the improved YOLOv8 network achieves a detection accuracy of 98.16% for tea pest detection, which is a 2.62% improvement over the original YOLOv8 network. Compared with the YOLOv10, YOLOv9, YOLOv7, Faster RCNN, and SSD models, the improved YOLOv8 network has increased the mAP value by 3.12%, 4.34%, 5.44%, 16.54%, and 11.29%, respectively, enabling fast and accurate identification of early-stage micro pests in tea gardens. This study proposes an improved YOLOv8 network model based on deep learning for the detection of micro-pests in tea, providing a viable research method and significant reference for addressing the identification of micro-pests in tea. It offers an effective pathway for the high-quality development of Yunnan’s ecological tea industry and ensures the healthy growth of the tea industry.

Keywords: AKConv; BIFormer; deep learning; improved YOLOv8; pest detection; SIoU (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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