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Predicting the Feed Intake of Cattle Based on Jaw Movement Using a Triaxial Accelerometer

文献类型： 外文期刊

作者： Ding, Luyu 1 ; Lv, Yang 1 ; Jiang, Ruixiang 1 ; Zhao, Wenjie 3 ; Li, Qifeng 1 ; Yang, Baozhu 1 ; Yu, Ligen 1 ; Ma, Weihong 1 ; Gao, Ronghua 1 ; Yu, Qinyang 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr NERCITA, Beijing 100097, Peoples R China

3.Solway Online Beijing New Energy Technol Co Ltd, Beijing 100191, Peoples R China

关键词： jaw movement; accelerometer; behavior classification; feed intake prediction; machine learning

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.408； 五年影响因子：3.459 ）

ISSN：

年卷期： 2022 年 12 卷 7 期

页码：

收录情况： SCI

摘要： The use of an accelerometer is considered as a promising method for the automatic measurement of the feeding behavior or feed intake of cattle, with great significance in facilitating daily management. To address further need for commercial use, an efficient classification algorithm at a low sample frequency is needed to reduce the amount of recorded data to increase the battery life of the monitoring device, and a high-precision model needs to be developed to predict feed intake on the basis of feeding behavior. Accelerograms for the jaw movement and feed intake of 13 mid-lactating cows were collected during feeding with a sampling frequency of 1 Hz at three different positions: the nasolabial levator muscle (P1), the right masseter muscle (P2), and the left lower lip muscle (P3). A behavior identification framework was developed to recognize jaw movements including ingesting, chewing and ingesting-chewing through extreme gradient boosting (XGB) integrated with the hidden Markov model solved by the Viterbi algorithm (HMM-Viterbi). Fourteen machine learning models were established and compared in order to predict feed intake rate through the accelerometer signals of recognized jaw movement activities. The developed behavior identification framework could effectively recognize different jaw movement activities with a precision of 99% at a window size of 10 s. The measured feed intake rate was 190 +/- 89 g/min and could be predicted efficiently using the extra trees regressor (ETR), whose R-2, RMSE, and NME were 0.97, 0.36 and 0.05, respectively. The three investigated monitoring sites may have affected the accuracy of feed intake prediction, but not behavior identification. P1 was recommended as the proper monitoring site, and the results of this study provide a reference for the further development of a wearable device equipped with accelerometers to measure feeding behavior and to predict feed intake.

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