Recognition of Cattle's Feeding Behaviors Using Noseband Pressure Sensor With Machine Learning - Chen, Guipeng; Li, Cong; Guo, Yang; Shu, Hang; Cao, Zhen; Xu, Beibei (Frontiers in Veterinary Science, 2022)
Automatic monitoring of feeding behavior especially rumination and eating in cattle is im-portant to keep track of animal health and growth condition as well as disease warning. The noseband pressure sensor is not only able to accurately sense the pressure change of the cattle’s jaw movements, which can directly reflect the cattle’s chewing behavior, but also has strong re-sistance to interference. However, it is difficult to keep the same initial pressure while wearing the pressure sensor, and this will pose a challenge to process the feeding behavior data. This pa-per proposed a machine learning approach aiming at eliminating the influence of initial pres-sure on the identification of rumination and eating behaviors. The method mainly used the local slope to obtain the local data variation and combined Fast Fourier Transform (FFT) to extract the frequency-domain features. Extreme Gradient Boosting Algorithm (XGB) was performed to clas-sify the features of rumination and eating behaviors. Experimental results showed that the local slope in combination with frequency-domain features achieved F1 score of 0.96, recognition ac-curacy of 0.966 and Cohen’s Kappa of 0.94 in both rumination and eating behaviors. Combined with the commonly used data processing algorithms and time-domain feature extraction meth-od, the proposed approach has improved the behavior recognition accuracy. This work will con-tribute to the standardized application and promotion of the noseband pressure sensors.