数字农科院2.0

YOLO-LSD: A lightweight scale dynamic model for tiny pest detection in tea plantations

文献类型： 外文期刊

作者： Lin Zhu;Weijie Peng;Li Zhou;Xiuxiu Xu;Zhihan Wang;Zhiwei Chen;Hewei Meng;Chunwang Dong

作者机构：

关键词： Lightweight;Scale-dynamic attention mechanism;Tiny pest detection;YOLO-LSD

期刊名称： Smart Agricultural Technology

ISSN： 2772-3755

年卷期： 2025 年 12 卷

页码：

收录情况： ESCI（2025版）

摘要： The tea green leafhopper (Empoasca pirisuga Matumura), a tiny pest of tea plantations, is a small target in the board's trapping image, while the complex environment of the tea plantation and the changing light conditions easily interfere with the model's judgment. This study presents YOLO-LSD, a lightweight scale dynamic feature detection model based on an improved YOLOv8 algorithm. Firstly, a Scale Dynamic Attention Module (SDAM) is incorporated into the backbone network. By calculating the standard deviation of the input feature map to measure its complexity, SDAM dynamically assigns weights to different convolutional kernel branches. This mechanism focuses on the key features and suppresses redundant information generated by lighting changes, thereby improving detection accuracy. Secondly, to improve the capture detail of tea green leafhopper, the detection head is optimized with a P2 head replacing the P5 head and integrating the SEAM attention mechanism. Finally, the Wavelet Pool module substitutes some convolution and sampling operations, reducing aliasing, preserving high-frequency details, and decreasing the computational cost. The experimental results demonstrate that the enhanced model attains mAP, F1, Precision, and Recall scores of 94.3 %, 92.1 %, 91.9 %, and 92.4 %, respectively. When evaluated in comparison with the original YOLOv8n model, these represent enhancements of 2.2 %, 1.9 %, 0.2 %, and 3.7 %. The model parameters and FLOPs were reduced by 1.32 million and 0.1 gigaflops, respectively, resulting in a model size of only 3.8 MB. This research provides a valuable reference for the precise identification of pests in tea plantations.

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