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Volume 22
Issue 9
10.3390/s22093271
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Article

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

by
Zhenwei Yu
1,
Yuehua Liu
2,3,
Sufang Yu
4,
Ruixue Wang
5,
Zhanhua Song
1,
Yinfa Yan
1,
Fade Li
1,
Zhonghua Wang
6 and
Fuyang Tian
1,*
1
College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
2
Shandong Provincial Key Laboratory of Horticultural Machineries and Equipment, Tai’an 271018, China
3
Shandong Provincial Engineering Laboratory of Agricultural Equipment Intelligence, Tai’an 271018, China
4
College of Life Sciences, Shangdong Agriculture University, Tai’an 271018, China
5
Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China
6
College of Animal Science and Technology, Shangdong Agriculture University, Tai’an 271018, China
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(9), 3271; https://doi.org/10.3390/s22093271
Submission received: 4 April 2022 / Revised: 22 April 2022 / Accepted: 22 April 2022 / Published: 24 April 2022
(This article belongs to the Topic Data Analytics and Machine Learning in Artificial Emotional Intelligence)
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Abstract

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.
Keywords: dairy cow; deep learning; DRN-YOLO; edge computing; feeding behaviour recognition dairy cow; deep learning; DRN-YOLO; edge computing; feeding behaviour recognition

Share and Cite

MDPI and ACS Style

Yu, Z.; Liu, Y.; Yu, S.; Wang, R.; Song, Z.; Yan, Y.; Li, F.; Wang, Z.; Tian, F. Automatic Detection Method of Dairy Cow Feeding Behaviour Based on YOLO Improved Model and Edge Computing. Sensors 2022, 22, 3271. https://doi.org/10.3390/s22093271

AMA Style

Yu Z, Liu Y, Yu S, Wang R, Song Z, Yan Y, Li F, Wang Z, Tian F. Automatic Detection Method of Dairy Cow Feeding Behaviour Based on YOLO Improved Model and Edge Computing. Sensors. 2022; 22(9):3271. https://doi.org/10.3390/s22093271

Chicago/Turabian Style

Yu, Zhenwei, Yuehua Liu, Sufang Yu, Ruixue Wang, Zhanhua Song, Yinfa Yan, Fade Li, Zhonghua Wang, and Fuyang Tian. 2022. "Automatic Detection Method of Dairy Cow Feeding Behaviour Based on YOLO Improved Model and Edge Computing" Sensors 22, no. 9: 3271. https://doi.org/10.3390/s22093271

APA Style

Yu, Z., Liu, Y., Yu, S., Wang, R., Song, Z., Yan, Y., Li, F., Wang, Z., & Tian, F. (2022). Automatic Detection Method of Dairy Cow Feeding Behaviour Based on YOLO Improved Model and Edge Computing. Sensors, 22(9), 3271. https://doi.org/10.3390/s22093271

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