Abstract
The behaviors of dairy cows, such as feeding, ruminating, running, resting (standing, lying), head-shaking, drinking, and walking, can indicate their health status. In this study, a multi-sensor was used to collect data of cow's multi-behaviors for research on behavior recognition. Firstly, a collar style data acquisition system equipped with geomagnetic and acceleration sensors to collect the behavioral data of dairy cows during their daily activities was designed. Secondly, the dairy cow behavioral recognition fusion model based on K-Nearest-Neighbors (KNN) and Random Forest (RF) models were used for behavior classification. To verify the accuracy of the fusion model, the algorithms of KNN, RF, Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Learning Vector Quantization (LVQ) were introduced for comparative recognition experiments with different algorithms. The KNN-RF fusion model had the highest average recognition accuracy of 98.51%, followed by the KNN model with an average recognition accuracy of 95.37%, and the LVQ model had the lowest average recognition accuracy of 80.81%. For the recognition and verification of each behavior, the KNN-RF fusion model had the most obvious improvement in the recognition of dairy cow feeding behavior, with a recognition accuracy of 99.34%, followed by the KNN model with a recognition accuracy of 95.07%. All six models had the lowest recognition accuracy for cow head-shaking behavior: a recognition accuracy of 89.11% with the KNN-RF model followed by the RF model with a recognition accuracy of 85.14%. The system can quickly and continuously collect cow behavior information, accurately recognize individual behaviors, and provide a scientific basis for the optimal design and efficient management of digital facilities and equipment for dairy cows.
Highlights
Dairy farming in China is developing rapidly towards intensive and large-scale production
The KNN-Random Forest (RF) fusion model had the highest recognition accuracy for the head-shaking behavior at 89.11%, followed by the KNN model with a recognition accuracy of 85.14%
When Gamma = 35 and C = 10, the Support Vector Machine (SVM) model had the highest recognition rate of 92.39%, and that of the Gradient Boosting Decision Tree (GBDT) model was 90.76%. 3) The KNN-RF fusion model had the highest accuracy of 98.51% for all cow behaviors, while the average recognition accuracy of feeding, ruminant, running, standing still, shaking-head, drinking and walking was 99.34%, 96.97%, 92.45%, 98.15%, 89.11%, 98.08%, 97.04%, respectively
Summary
Dairy farming in China is developing rapidly towards intensive and large-scale production. The physical health, breeding status, feed intake, and physiological indicators of individual dairy cows affect the sustainable development of the dairy industry and the economic interests of dairy farmers. The modernization level of individual cow behavior monitoring equipment in China is generally low. Rumination is an essential physiological activity of dairy cows that is closely related to their milk production and reproductive performance, and this reflects the health status of dairy cows to a certain extent[1]. Feeding and activities can affect the nutritional status of cows. Traditional manual monitoring methods are labor-intensive and will perturb dairy behavior. Compared with traditional methods, such a VOLUME XX, 2017
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