YOLO-CG-HS: A lightweight spore detection method for wheat airborne fungal pathogens

文献类型： 外文期刊

第一作者： Cheng, Tao

作者： Cheng, Tao;Zhang, Dongyan;Niu, Zhen;Xie, Jiyuan;Gu, Chunyan;Yang, Xue;Zhou, Xin-Gen;Qiao, Hongbo;Guo, Wei

作者机构：

关键词： Lightweight model; Wheat; Air-borne disease; Spore concentration; Detection

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2024 年 227 卷

页码：

收录情况： SCI

摘要： The rapid, accurate and real-time online detection of spore concentration of various airborne pathogens in field crops is of great significance in guiding agricultural producers scientifically, enabling them to forecast disease development and implement timely preventive and control measures. This study presents a quantitative spore detection method for two prevalent wheat airborne fungal diseases using the YOLO-CG-HS lightweight model. Initially, the lightweight Context Guided module (CG) is integrated into the original Backbone of YOLOv5s to enhance the capture of global and edge information in spore images. Subsequently, the High-level Screeningfeature Pyramid Networks (HS-FPN) module is incorporated into the Head to better integrate multi-scale feature information of spores, thereby improving the model's detection performance and ability to capture spore microtargets. The model's robustness is then tested across various scenarios, including different shapes, densities, and complex backgrounds. Results indicate that the inclusion of both the CG module and the HS-FPN module into the original baseline model significantly reduces the number of model parameters to only 1.21 M. The model's average precision (mAP) stands at 95.9 %, with an FPS of 152.5, maintaining performance levels similar to the original model. Moreover, the designed model effectively addresses the challenge of identifying difficult and missed cases resulting from spore adhesion and overlap in various airborne wheat diseases. The YOLO-CG-HS lightweight model developed in this study accurately detects various types of pathogen spores while balancing parameters, efficiency, and accuracy. This offers crucial technical support for the model migration and application of low-cost and high-precision embedded field spore capture instruments.

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