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Article

Machine-Learning Techniques Can Enhance Dairy Cow Estrus Detection Using Location and Acceleration Data

1
School of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
2
School of Biosciences, The University of Nottingham, Sutton Bonington, Loughborough LE12 5RD, UK
3
Key Laboratory for Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Animals 2020, 10(7), 1160; https://doi.org/10.3390/ani10071160
Received: 30 March 2020 / Revised: 25 May 2020 / Accepted: 7 July 2020 / Published: 8 July 2020
(This article belongs to the Special Issue Animal-Centered Computing)
We investigated the feasibility of combing location, acceleration, and machine learning technologies to accurately detect dairy cows in estrus. An automatic data acquisition system was developed to continuously monitor the location and acceleration data of cow activities. Estrus indicators were obtained by principal component analysis (PCA) of twelve behavioral metrics generated from the collected data sets, which were: duration of standing, duration of lying, duration of walking, duration of feeding, duration of drinking, switching times between activity and lying, steps, displacement, average velocity, walking times, feeding times, drinking times. We introduced K-nearest neighbor (KNN), back-propagation neural network (BPNN), linear discriminant analysis (LDA), classification and regression tree (CART) algorithms for the estrus identification of cows. A comparative assessment of the integration of algorithms and time windows was performed to for determining the optimal combination. The results achieving in this study suggest that synthesis of location, acceleration, and machine learning methods can be utilized to improve estrus cow detection.
The aim of this study was to assess combining location, acceleration and machine learning technologies to detect estrus in dairy cows. Data were obtained from 12 cows, which were monitored continuously for 12 days. A neck mounted device collected 25,684 records for location and acceleration. Four machine-learning approaches were tested (K-nearest neighbor (KNN), back-propagation neural network (BPNN), linear discriminant analysis (LDA), and classification and regression tree (CART)) to automatically identify cows in estrus from estrus indicators determined by principal component analysis (PCA) of twelve behavioral metrics, which were: duration of standing, duration of lying, duration of walking, duration of feeding, duration of drinking, switching times between activity and lying, steps, displacement, average velocity, walking times, feeding times, and drinking times. The study showed that the neck tag had a static and dynamic positioning accuracy of 0.25 ± 0.06 m and 0.45 ± 0.15 m, respectively. In the 0.5-h, 1-h, and 1.5-h time windows, the machine learning approaches ranged from 73.3 to 99.4% for sensitivity, from 50 to 85.7% for specificity, from 77.8 to 95.8% for precision, from 55.6 to 93.7% for negative predictive value (NPV), from 72.7 to 95.4% for accuracy, and from 78.6 to 97.5% for F1 score. We found that the BPNN algorithm with 0.5-h time window was the best predictor of estrus in dairy cows. Based on these results, the integration of location, acceleration, and machine learning methods can improve dairy cow estrus detection. View Full-Text
Keywords: dairy cow; estrus detection; location; accelerometer; principal component analysis; machine learning techniques dairy cow; estrus detection; location; accelerometer; principal component analysis; machine learning techniques
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MDPI and ACS Style

Wang, J.; Bell, M.; Liu, X.; Liu, G. Machine-Learning Techniques Can Enhance Dairy Cow Estrus Detection Using Location and Acceleration Data. Animals 2020, 10, 1160. https://doi.org/10.3390/ani10071160

AMA Style

Wang J, Bell M, Liu X, Liu G. Machine-Learning Techniques Can Enhance Dairy Cow Estrus Detection Using Location and Acceleration Data. Animals. 2020; 10(7):1160. https://doi.org/10.3390/ani10071160

Chicago/Turabian Style

Wang, Jun, Matt Bell, Xiaohang Liu, and Gang Liu. 2020. "Machine-Learning Techniques Can Enhance Dairy Cow Estrus Detection Using Location and Acceleration Data" Animals 10, no. 7: 1160. https://doi.org/10.3390/ani10071160

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