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

Author

Listed:
  • Luyu Ding

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China)

  • Yang Lv

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China)

  • Ruixiang Jiang

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China)

  • Wenjie Zhao

    (Solway Online (Beijing) New Energy Technology Co., Ltd., Beijing 100191, China)

  • Qifeng Li

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China)

  • Baozhu Yang

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China)

  • Ligen Yu

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China)

  • Weihong Ma

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China)

  • Ronghua Gao

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China)

  • Qinyang Yu

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China)

Abstract

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.

Suggested Citation

  • Luyu Ding & Yang Lv & Ruixiang Jiang & Wenjie Zhao & Qifeng Li & Baozhu Yang & Ligen Yu & Weihong Ma & Ronghua Gao & Qinyang Yu, 2022. "Predicting the Feed Intake of Cattle Based on Jaw Movement Using a Triaxial Accelerometer," Agriculture, MDPI, vol. 12(7), pages 1-18, June.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:7:p:899-:d:843858
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    References listed on IDEAS

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    1. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
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    Cited by:

    1. Yusei Kawagoe & Ikuo Kobayashi & Thi Thi Zin, 2023. "Facial Region Analysis for Individual Identification of Cows and Feeding Time Estimation," Agriculture, MDPI, vol. 13(5), pages 1-15, May.
    2. Na Liu & Jingwei Qi & Xiaoping An & Yuan Wang, 2023. "A Review on Information Technologies Applicable to Precision Dairy Farming: Focus on Behavior, Health Monitoring, and the Precise Feeding of Dairy Cows," Agriculture, MDPI, vol. 13(10), pages 1-21, September.

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