A Non-Contact Cow Estrus Monitoring Method Based on the Thermal Infrared Images of Cows

Zhen Wang, Shuai Wang, Chunguang Wang, Yong Zhang, Zheying Zong (), Haichao Wang, Lide Su and Yingjie Du
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Zhen Wang: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Autonomous Region, Hohhot 010018, China
Shuai Wang: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Autonomous Region, Hohhot 010018, China
Chunguang Wang: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Autonomous Region, Hohhot 010018, China
Yong Zhang: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Autonomous Region, Hohhot 010018, China
Zheying Zong: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Autonomous Region, Hohhot 010018, China
Haichao Wang: College of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Inner Mongolia Autonomous Region, Hohhot 010018, China
Lide Su: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Autonomous Region, Hohhot 010018, China
Yingjie Du: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Autonomous Region, Hohhot 010018, China

Agriculture, 2023, vol. 13, issue 2, 1-19

Abstract: Traditional methods of cow estrus monitoring technology are not suitable for the current needs of large-scale, intensive and welfare-based farming. There is a need to improve the detection rate of cow estrus and to reduce the emergency response caused by wearing contact devices. Furthermore, it is necessary to verify the practical effectiveness of the LOGISITC and SV (support vector machine) models for modeling cow estrus. In this paper, we have proposed a non-contact cow estrus monitoring method based on the thermal infrared images of cows and have proposed a lab-color-space-based feature extraction method for the thermal infrared images of cow eyes and vulvas. The test subjects were 10 Holstein cows, monitored on a fixed basis, to determine the best segmentation contour. The LOGISTIC and SVM (support vector machine) models were used to establish the cow estrus model using the thermal infrared temperature variation in cows in estrus and cows not in estrus. The experimental results showed that the heat detection rate of the LOGISTIC-based model was 82.37% and the heat detection rate of the SVM-based model was 81.42% under the optimal segmentation profile. The highest temperature in the eye and vulva of cows was the input, and the recall rate was above 86%. The heat monitoring method based on thermal infrared images does not cause stress to cows and meets the needs of modern dairy farming for welfare breeding.

Keywords: dairy cattle; estrus monitoring; thermal infrared images; lab color space; LOGISTIC; SVM (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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