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

• Published in: Agriculture (Basel) Vol. 14; no. 10; p. 1710
• Main Authors: Li, Yane, Chen, Ting, Xia, Fang, Feng, Hailin, Ruan, Yaoping, Weng, Xiang, Weng, Xiaoxing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2024
• Subjects: Accuracy; Algorithms; Artificial intelligence; complex environments; Datasets; Deep learning; Gardens & gardening; Identification; Leaves; Machine learning; multi-scale tea tree pest; Object recognition; Parameter identification; pest recognition; Pests; Real time; Recall; Similarity; Support vector machines; Telecommunication systems; Trees; YOLOv7-tiny; China
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture14101710

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.