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Recognising Cattle Behaviour with Deep Residual Bidirectional LSTM Model Using a Wearable Movement Monitoring Collar

Author

Listed:
  • Yiqi Wu

    (College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China
    These authors contributed equally to this work.)

  • Mei Liu

    (College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China
    These authors contributed equally to this work.)

  • Zhaoyuan Peng

    (College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China)

  • Meiqi Liu

    (College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China)

  • Miao Wang

    (College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China)

  • Yingqi Peng

    (College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China)

Abstract

Cattle behaviour is a significant indicator of cattle welfare. With the advancements in electronic equipment, monitoring and classifying multiple cattle behaviour patterns is becoming increasingly important in precision livestock management. The aim of this study was to detect important cattle physiological states using a neural network model and wearable electronic sensors. A novel long short-term memory (LSTM) recurrent neural network model that uses two-way information was developed to accurately classify cattle behaviour and compared with baseline LSTM. Deep residual bidirectional LSTM and baseline LSTM were used to classify six behavioural patterns of cows with window sizes of 64, 128 and 256 (6.4 s, 12.8 s and 25.6 s, respectively). The results showed that when using deep residual bidirectional LSTM with window size 128, four classification performance indicators, namely, accuracy, precision, recall, and F1-score, achieved the best results of 94.9%, 95.1%, 94.9%, and 94.9%, respectively. The results showed that the deep residual bidirectional LSTM model can be used to classify time-series data collected from twelve cows using inertial measurement unit collars. Six aim cattle behaviour patterns can be classified with high accuracy. This method can be used to quickly detect whether a cow is suffering from bovine dermatomycosis. Furthermore, this method can be used to implement automated and precise cattle behaviour classification techniques for precision livestock farming.

Suggested Citation

  • Yiqi Wu & Mei Liu & Zhaoyuan Peng & Meiqi Liu & Miao Wang & Yingqi Peng, 2022. "Recognising Cattle Behaviour with Deep Residual Bidirectional LSTM Model Using a Wearable Movement Monitoring Collar," Agriculture, MDPI, vol. 12(8), pages 1-13, August.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1237-:d:890178
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    References listed on IDEAS

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    1. Yu Zhao & Rennong Yang & Guillaume Chevalier & Ximeng Xu & Zhenxing Zhang, 2018. "Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-13, December.
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    Cited by:

    1. Yuxiang Yang & Yifan Deng & Jiazhou Li & Meiqi Liu & Yao Yao & Zhaoyuan Peng & Luhui Gu & Yingqi Peng, 2024. "An Effective Yak Behavior Classification Model with Improved YOLO-Pose Network Using Yak Skeleton Key Points Images," Agriculture, MDPI, vol. 14(10), pages 1-19, October.

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