Development of a threshold-based classifier for real-time recognition of cow feeding and standing behavioural activities from accelerometer data

Arcidiacono, Claudia; Porto, Simona M.C.; Mancino, Massimo; Cascone, Giovanni

doi:10.1016/j.compag.2017.01.021

Cited by 105 publications

(52 citation statements)

References 14 publications

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“…For example, Tullo et al (13) found that cows commonly lie down for about 9-14 h/day when reared indoors, while Lovarelli et al (14) showed that seasonality affects the lying time, in particular that the monitored cows lie down for a longer period in the cold season (on average 12.06 h/day) than in summer (on average 10.04 h/day). This supports also the findings in which a lower lying time is related to high THI conditions and in which high THI is associated with lower feed intake, lower milk production, and variations in behavior (9,11,17,25,26). Such a result was also obtained in this study, where it was found that one of the factors affecting lying time is seasonality and where the lactation stage (DIM), illuminance, THI, BCS, and milk production are the most relevant factors.…”

Section: Discussionsupporting

confidence: 91%

Relating Lying Behavior With Climate, Body Condition Score, and Milk Production in Dairy Cows

et al. 2020

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Attention on animal behavior and welfare has been increasing. Scientific knowledge about the effect of behavior and welfare on animals' production augmented and made clear the need of improving their living conditions. Among the variables to monitor in dairy cattle farming, lying time represents a signal for health and welfare status as well as for milk production. The aim of this study is to identify the relationship among the lying behavior of dairy cows and milk production, body condition score (BCS), weather variables, and the temperature-humidity index (THI) in the barn from a dairy farm located in Northern Italy. One-year data were collected on this farm with sensors that allowed monitoring of the environmental conditions in the barn and the activity of primiparous lactating cows. Principal components analysis (PCA), factor analysis (FA), generalized linear model select (GLMSelect), and logistic analysis (LA) were carried out to get the relationships among variables. Among the main results, it emerges that the effect of weather parameters is quite restrained, except for THI > 70, which negatively affects the lying time. In addition, the most productive cows are found to lie down more than the less productive ones, and the parameters of milk production, lying time, and BCS are found to be linked by a similar trend.

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Section: Discussionsupporting

confidence: 91%

Relating Lying Behavior With Climate, Body Condition Score, and Milk Production in Dairy Cows

et al. 2020

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“…For a real implementation at large scale, using IT as predictor instead of DMI has great advantages in terms of costs and setting. Successful results on real-time determination of IT by means of accelerometers (Oudshoorn et al, 2013, Arcidiacono et al, 2017 are encouraging to make of our software sensor a feasible and low cost solution for on farm applications in the future. Recently, it has been suggested that, for a stable management of feed allocation, the diurnal pattern of methane is constant over time (Bell et al, 2018), which, with respect to our modelling work, translates into a constant diurnal feeding behaviour pattern.…”

Section: Discussionmentioning

confidence: 85%

A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle

Muñoz-Tamayo

Agudelo

Dewhurst

et al. 2019

Animal

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Large efforts have been deployed in developing methods to estimate methane emissions from cattle. For large scale applications, accurate and inexpensive methane predictors are required. Within a livestock precision farming context, the objective of this work was to integrate real-time data on animal feeding behaviour with an in silico model for predicting the individual dynamic pattern of methane emission in cattle. The integration of real-time data with a mathematical model to predict variables that are not directly measured constitutes a software sensor. We developed a dynamic parsimonious grey-box model that uses as predictor variables either dry matter intake (DMI) or the intake time (IT). The model is described by ordinary differential equations.Model building was supported by experimental data of methane emissions from respiration chambers. The data set comes from a study with finishing beef steers (cross-bred Charolais and purebred Luing finishing). Dry matter intake and IT were recorded using feed bins. For research purposes, in this work, our software sensor operated off-line. That is, the predictor variables (DMI, IT) were extracted from the recorded data (rather than from an on-line sensor). A total of 37 individual dynamic patterns of methane production were analyzed. Model performance was assessed by concordance analysis between the predicted methane output and the methane measured in respiration chambers. The model predictors DMI and IT performed similarly with a Lin’s concordance correlation coefficient (CCC) of 0.78 on average. When predicting the daily methane production, the CCC was 0.99 for both DMI and IT predictors. Consequently, on the basis of concordance analysis, our model performs very well compared with reported literature results for methane proxies and predictive models. As IT measurements are easier to obtain than DMI measurements, this study suggests that a software sensor that integrates our in silico model with a real-time sensor providing accurate IT measurements is a viable solution for predicting methane output in a large scale context.

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“…In the conducted experiments, feeding ecology of four livestock species under different management in a semi-arid pastoral system is assessed. In [67], the development of a threshold-based classifier for real-time recognition of cow feeding and standing behavioral activities from accelerometer data is presented. In [68], the use of sensors combining local positioning and acceleration is used to measure feeding behavior differences associated with lameness in dairy cattle.…”

Section: Feeding Ecologymentioning

confidence: 99%

Internet of Things for evaluating foraging and feeding behavior of cattle on grassland-based farming systems: concepts and review of sensor technologies

Álvarez¹,

Valdés²,

Perez-Teruel³

2019

Proceedings of ISP RAS

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In this paper, we give an overview of the movement, foraging and feeding ecology as well as sensors technologies that could be embedded into an IoT-based platform for Precision Livestock Farming (PLF). A total of 43 peer-reviewed journal papers indexed by Web of Science were surveyed. Firstly, sensors technologies (e.g., RFID, GPS, or Accelerometer) used by the authors of each paper were identified. Then, papers were classified according to their applicability to ecological studies in the fields of foraging and feeding behavior.

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Development of a threshold-based classifier for real-time recognition of cow feeding and standing behavioural activities from accelerometer data

Cited by 105 publications

References 14 publications

Relating Lying Behavior With Climate, Body Condition Score, and Milk Production in Dairy Cows

Relating Lying Behavior With Climate, Body Condition Score, and Milk Production in Dairy Cows

A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle

Internet of Things for evaluating foraging and feeding behavior of cattle on grassland-based farming systems: concepts and review of sensor technologies

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