TTPRNet: A Real-Time and Precise Tea Tree Pest Recognition Model in Complex Tea Garden Environments

Yane Li, Ting Chen, Fang Xia (), Hailin Feng (), Yaoping Ruan, Xiang Weng and Xiaoxing Weng
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Yane Li: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Ting Chen: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Fang Xia: College of Economics and Management, Zhejiang A&F University, Hangzhou 311300, China
Hailin Feng: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Yaoping Ruan: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Xiang Weng: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Xiaoxing Weng: Research Institute of Tea Resources Utilization and Agricultural Products Processing Technology, Zhejiang Academy of Agricultural Machinery, Jinhua 321017, China

Agriculture, 2024, vol. 14, issue 10, 1-23

Abstract: The accurate identification of tea tree pests is crucial for tea production, as it directly impacts yield and quality. In natural tea garden environments, identifying pests is challenging due to their small size, similarity in color to tea trees, and complex backgrounds. To address this issue, we propose TTPRNet, a multi-scale recognition model designed for real tea garden environments. TTPRNet introduces the ConvNext architecture into the backbone network to enhance the global feature learning capabilities and reduce the parameters, and it incorporates the coordinate attention mechanism into the feature output layer to improve the representation ability for different scales. Additionally, GSConv is employed in the neck network to reduce redundant information and enhance the effectiveness of the attention modules. The NWD loss function is used to focus on the similarity between multi-scale pests, improving recognition accuracy. The results show that TTPRNet achieves a recall of 91% and a mAP of 92.8%, representing 7.1% and 4% improvements over the original model, respectively. TTPRNet outperforms existing object detection models in recall, mAP, and recognition speed, meeting real-time requirements. Furthermore, the model integrates a counting function, enabling precise tallying of pest numbers and types and thus offering practical solutions for accurate identification in complex field conditions.

Keywords: multi-scale tea tree pest; pest recognition; complex environments; YOLOv7-tiny (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 complete reference list from CitEc
Citations: View citations in EconPapers (1)

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