Automatic Detection Method of Dairy Cow Feeding Behaviour Based on YOLO Improved Model and Edge Computing

文献类型： 外文期刊

第一作者： Yu, Zhenwei

作者： Yu, Zhenwei;Song, Zhanhua;Yan, Yinfa;Li, Fade;Tian, Fuyang;Liu, Yuehua;Liu, Yuehua;Yu, Sufang;Wang, Ruixue;Wang, Zhonghua

作者机构： Shandong Agr Univ, Coll Mech & Elect Engn, Tai An 271018, Shandong, Peoples R China;Shandong Prov Key Lab Hort Machineries & Equipmen, Tai An 271018, Shandong, Peoples R China;Shandong Prov Engn Lab Agr Equipment Intelligence, Tai An 271018, Shandong, Peoples R China;Shangdong Agr Univ, Coll Life Sci, Tai An 271018, Shandong, Peoples R China;Chinese Acad Agr Mechanizat Sci, Beijing 100083, Peoples R China;Shangdong Agr Univ, Coll Anim Sci & Technol, Tai An 271018, Shandong, Peoples R China

关键词： dairy cow; deep learning; DRN-YOLO; edge computing; feeding behaviour recognition

期刊名称：SENSORS （ 2021影响因子：3.847； 五年影响因子：4.05 ）

ISSN：

年卷期： 2022 年 22 卷 9 期

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收录情况： SCI

摘要： The feeding behaviour of cows is an essential sign of their health in dairy farming. For the impression of cow health status, precise and quick assessment of cow feeding behaviour is critical. This research presents a method for monitoring dairy cow feeding behaviour utilizing edge computing and deep learning algorithms based on the characteristics of dairy cow feeding behaviour. Images of cow feeding behaviour were captured and processed in real time using an edge computing device. A DenseResNet-You Only Look Once (DRN-YOLO) deep learning method was presented to address the difficulties of existing cow feeding behaviour detection algorithms' low accuracy and sensitivity to the open farm environment. The deep learning and feature extraction enhancement of the model was improved by replacing the CSPDarknet backbone network with the self-designed DRNet backbone network based on the YOLOv4 algorithm using multiple feature scales and the Spatial Pyramid Pooling (SPP) structure to enrich the scale semantic feature interactions, finally achieving the recognition of cow feeding behaviour in the farm feeding environment. The experimental results showed that DRN-YOLO improved the accuracy, recall, and mAP by 1.70%, 1.82%, and 0.97%, respectively, compared to YOLOv4. The research results can effectively solve the problems of low recognition accuracy and insufficient feature extraction in the analysis of dairy cow feeding behaviour by traditional methods in complex breeding environments, and at the same time provide an important reference for the realization of intelligent animal husbandry and precision breeding.

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