The Early Prediction of Common Disorders in Dairy Cows Monitored by Automatic Systems with Machine Learning Algorithms
Xiaojing Zhou,
Chuang Xu,
Hao Wang,
Wei Xu,
Zixuan Zhao,
Mengxing Chen,
Bin Jia,
Baoyin Huang
Affiliations
Xiaojing Zhou
Department of Information and Computing Science, Heilongjiang Bayi Agricultural University, No. 5 Xinyang Road, Daqing 163319, China
Chuang Xu
Heilongjiang Provincial Key Laboratory of Prevention and Control of Bovine Diseases, College of Animal Science and Veterinary Medicine, Heilongjiang Bayi Agricultural University, No. 5 Xinyang Road, Daqing 163319, China
Hao Wang
Animal Husbandry and Veterinary Branch of Heilongjiang Academy of Agricultural Science, Qiqihaer 161005, China
Wei Xu
Department of Biosystems, Division of Animal and Human Health Engineering, KU Leuven, 3000 Leuven, Belgium
Zixuan Zhao
Heilongjiang Provincial Key Laboratory of Prevention and Control of Bovine Diseases, College of Animal Science and Veterinary Medicine, Heilongjiang Bayi Agricultural University, No. 5 Xinyang Road, Daqing 163319, China
Mengxing Chen
Heilongjiang Provincial Key Laboratory of Prevention and Control of Bovine Diseases, College of Animal Science and Veterinary Medicine, Heilongjiang Bayi Agricultural University, No. 5 Xinyang Road, Daqing 163319, China
Bin Jia
Animal Husbandry and Veterinary Branch of Heilongjiang Academy of Agricultural Science, Qiqihaer 161005, China
Baoyin Huang
Animal Husbandry and Veterinary Branch of Heilongjiang Academy of Agricultural Science, Qiqihaer 161005, China
We use multidimensional data from automated monitoring systems and milking systems to predict disorders of dairy cows by employing eight machine learning algorithms. The data included the season, days in milking, parity, age at the time of disorders, milk yield (kg/day), activity (unitless), six variables related to rumination time, and two variables related to the electrical conductivity of milk. We analyze 131 sick cows and 149 healthy cows with identical lactation days and parity; all data are collected on the same day, which corresponds to the diagnosis day for disordered cows. For disordered cows, each variable, except the ratio of rumination time from daytime to nighttime, displays a decreasing/increasing trend from d-7 or d-3 to d0 and/or d-1, with the d0, d-1, or d-2 values reaching the minimum or maximum. The test data sensitivity for three algorithms exceeded 80%, and the accuracies of the eight algorithms ranged from 65.08% to 84.21%. The area under the curve (AUC) of the three algorithms was >80%. Overall, Rpart best predicts the disorders with an accuracy, precision, and AUC of 81.58%, 92.86%, and 0.908, respectively. The machine learning algorithms may be an appropriate and powerful decision support and monitoring tool to detect herds with common health disorders.
