Development of a methodological framework for a robust prediction of the main behaviours of dairy cows using a combination of machine learning algorithms on accelerometer data

Riaboff, Lucile; Poggi, Sylvain; Madouasse, Aurélien; Couvreur, Sébastien; Aubin, Sébastien; Bédère, Nicolas; Goumand, Etienne; Chauvin, Alain; Plantier, Guy

doi:10.1016/j.compag.2019.105179

Cited by 65 publications

(77 citation statements)

References 43 publications

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“…This step aimed to predict the behaviors successively expressed by the 26 dairy cows over the experiment. We used a method described in two previous studies which allows a prediction of the main behaviors of dairy cows on pasture from accelerometer data with a high reliability (accuracy: 98%; Cohen’s Kappa: 0.96) [ 14 , 15 ]. The six predicted behaviors are the following: Grazing: biting, taking frequent bites or chewing and searching without raising the head.…”

Section: Methodsmentioning

confidence: 99%

“…The steps carried out to develop this methodology are outlined in Appendix A ( Figure A1 ) and we refer to Riaboff et al [ 14 , 15 ] for a detailed description. The successive steps applied in the present study are illustrated in Figure 3 .…”

Section: Methodsmentioning

confidence: 99%

“…This pre-processing step was performed in Matlab R2018a. The eXtreme Gradient Boosting (XGB) model fitted by Riaboff et al [ 14 ] was then directly used to predict the behaviors using the xgboost package [ 22 ] in R 3.6.1 [ 23 ]. At the end of this step, the behavior of each cow throughout every successive window of 10 s was predicted for all 5 days of grazing.…”

Section: Methodsmentioning

confidence: 99%

“…The predicted behaviors in successive windows from the same cow over the entire experiment were finally smoothed using the hidden Markov model (HMM)-based Viterbi algorithm [ 24 , 25 ] applied with the R package HMM [ 26 ]. The HMM used to apply the Viterbi algorithm in this study was reported by Riaboff et al [ 14 ].…”

Section: Methodsmentioning

confidence: 99%

“…In two previous studies, a methodological framework was developed to predict a large range of behaviors of dairy cows on pasture from accelerometer data every 10 s, including grazing, walking, resting and ruminating both in lying and standing postures [ 14 , 15 ]. These predicted behaviors could now be combined with the position of the cows using the GPS data collected on animals.…”

Section: Introductionmentioning

confidence: 99%

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Use of Predicted Behavior from Accelerometer Data Combined with GPS Data to Explore the Relationship between Dairy Cow Behavior and Pasture Characteristics

Riaboff

Couvreur

Madouasse

et al. 2020

Sensors

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Our aim in this study was to investigate whether the behaviors of dairy cows on pasture, predicted with accelerometer data and combined with GPS data, can be used to better understand the relationship between behaviors and pasture characteristics. During spring 2018, 26 Holstein cows were equipped with a 3D-accelerometer and a GPS sensor fixed on a neck-collar for five days. The cows grazed alternatively in permanent and in temporary grasslands. The structural elements, soil moisture, slope and botanical characteristics were identified. Behaviors were predicted every 10 s from the accelerometer data and combined with the GPS data. The time-budgets expressed in each characterized zone of 8 m × 8 m were calculated. The relation between the time-budgets and pasture characteristics was explored with a linear mixed model. In the permanent grassland, dairy cows spent more time under a tree to ruminate (p < 0.001) and to rest (p < 0.001) and more time to graze in areas with Holcus lanatus (p < 0.001). In the temporary grassland, behavior was influenced by the external environment (presence of other animals on the farm; p < 0.05). Thus, this methodology seems relevant to better understand the relationship between the behaviors of dairy cows and grazing conditions to develop precision grazing.

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