文献类型： 外文期刊

作者： Li, Yane ¹ ; Chen, Ting ¹ ; Xia, Fang ⁴ ; Feng, Hailin ¹ ; Ruan, Yaoping ¹ ; Weng, Xiang ¹ ; Weng, Xiaoxing ⁵ ;

作者机构： 1.Zhejiang A&F Univ, Coll Math & Comp Sci, Hangzhou 311300, Peoples R China

2.Key Lab Forestry Intelligent Monitoring & Informat, Hangzhou 311300, Peoples R China

3.Key Lab State Forestry & Grassland Adm Forestry Se, Hangzhou 311300, Peoples R China

4.Zhejiang A&F Univ, Coll Econ & Management, Hangzhou 311300, Peoples R China

5.Zhejiang Acad Agr Machinery, Res Inst Tea Resources Utilizat & Agr Prod Proc Te, Jinhua 321017, Peoples R China

关键词： multi-scale tea tree pest; pest recognition; complex environments; YOLOv7-tiny

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 10 期

页码：

收录情况： SCI

摘要： 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.