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

Yiqi Wu, Mei Liu, Zhaoyuan Peng, Meiqi Liu, Miao Wang and Yingqi Peng ()
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Yiqi Wu: College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China
Mei Liu: College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China
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

Agriculture, 2022, vol. 12, issue 8, 1-13

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.

Keywords: wearable sensor; behaviour pattern classification; cattle; bidirectional long short-term memory; recurrent neural network; inertial measurement unit (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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