Machine-Learning Techniques Can Enhance Dairy Cow Estrus Detection Using Location and Acceleration Data
School of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
School of Biosciences, The University of Nottingham, Sutton Bonington, Loughborough LE12 5RD, UK
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.
Received: 30 March 2020
Revised: 25 May 2020
Accepted: 7 July 2020
Published: 8 July 2020
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.