Sensor data on cow activity, rumination, and ear temperature improve prediction of the start of calving in dairy cows

Rutten, C.J.; Kamphuis, C.; Hogeveen, H.; Huijps, K.; Nielen, M.; Steeneveld, W.

doi:10.1016/j.compag.2016.11.009

Cited by 74 publications

(71 citation statements)

References 40 publications

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“…To solve this problem, recently the use of three-axis accelerometers as a behavior-monitoring tool has been increasing; they have also been used for monitoring daily physical exercise for human health management purposes (Ohkawara et al, 2011;Oshima et al, 2010). In cattle there have been various reports using this tool: the estimation of grass intake at pasture (Oudshoorn et al, 2013), the prediction of calving with ear tag sensors (Rutten et al, 2017), behavior detection by a sensor attached to the mandible (Watanabe, Sakanoue, Kawamura, & Kozakai, 2008), the detection of various behaviors and lameness using sensors attached to the neck and a leg (Scheibe & Gromann, 2006).…”

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confidence: 99%

Dairy cattle behavior classifications based on decision tree learning using 3‐axis neck‐mounted accelerometers

Tamura

Okubo

Deguchi

et al. 2019

Animal Science Journal

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Demand has been increasing recently for an automated monitoring system of animal behavior as a tool for the management of livestock animals. This study investigated the association between the behavior of dairy cattle and the acceleration data collected using three‐axis neck‐mounted accelerometers, as well as the feasibility of improving the precision of behavior classifications through machine learning. In total 38 Holstein dairy cows were used, and kept in four different farms. A logger was mounted to each collar to obtain acceleration data for calculating the activity level and variations. At the same time the behavior of the cattle was observed visually. Characteristic acceleration waves were recorded for eating, rumination, and lying, respectively; and the activity level and variations were significantly different among these behaviors (p < 0.01). Decision tree learning was performed on the data set from Farm A and validated its precision; which proved to be 99.2% in cross‐validation, and 100% in test data sets from Farms B to D. This study showed that highly precise classifications for eating, rumination, and lying is possible by using decision tree learning to calculate the activity level and variations of cattle based on the data obtained by three‐axis accelerometers mounted to a collar.

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confidence: 99%

Dairy cattle behavior classifications based on decision tree learning using 3‐axis neck‐mounted accelerometers

Tamura

Okubo

Deguchi

et al. 2019

Animal Science Journal

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“…This sensor measures activity and rumination activity and is already used for estrus detection (estrus sensor). Two time windows were assumed with 21.2% or 54.4% sensitivity and specificity was assumed to be 99% (Rutten, et al, 2017). The "estrus sensor" was already in use so no additional investment or labor for attachment of the sensors was assumed.…”

Section: Model Structurementioning

confidence: 99%

“…Such sensors could potentially be used as an alternative to visual observation for onset and progression of calving , Ouellet, et al, 2016, Rutten, et al, 2017, Titler, et al, 2015. For instance, the walking behaviour and postures of cows prior to calving have been analyzed with video camera's (Cangar, et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Another example is temperature, which has been shown to decrease before the start of calving and this can be monitored with sensors (Burfeind, et al, 2011, Saint-Dizier andChastantMaillard, 2015). Lastly, changes in rumination, lying time and lying bouts determined by sensors have been associated with the start of calving (Bar and Solomon, 2010, Bucher and Sundrum, 2014, Ouellet, et al, 2016, Rutten, et al, 2017, Titler, et al, 2015.…”

Section: Introductionmentioning

confidence: 99%

“…But, the system was assumed to be already in use, so no additional investment or labor was required to use the sensor system. An example of such a sensor is a sensor used for estrus detection to which calving detection was added as additional feature (Rutten, et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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The core objective of the wireless sensor networks (WSNs) in the livestock industry is monitoring the state and activities of different animals for their well-being. Being energy-constrained, the consumption of these tiny devices needs to be optimized. Several techniques have been developed for making clusters in WSNs. The sensor nodes attached to the animals are mobile and dynamic in nature. Cluster heads (CHs) are selected to manage communication within a cluster. For efficient working of a network, the lifetime of clusters should be longer and there must always be a minimum number of clusters to make a network operational. This paper presents a metaheuristic artificial intelligence technique based on the social behavior of gray wolves to reduce the energy consumption of WSNs in the livestock industry. This nature-inspired clustering algorithm provides the robust and smooth communication for WSNs in the livestock industry. The grid size, energy level, direction, and transmission range are the key parameters used to measure the performance of algorithm.Results are compared with other well-known similar nature-inspired optimization algorithms such as comprehensive learning particle swarm optimization (CLPSO) and ant colony optimization-based clustering (CACONET). The simulation results exhibit the superiority of grey wolf optimizer in energy efficiency, cost effectiveness, and CH selection than CACONET and CLPSO.

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Sensor data on cow activity, rumination, and ear temperature improve prediction of the start of calving in dairy cows

Cited by 74 publications

References 40 publications

Dairy cattle behavior classifications based on decision tree learning using 3‐axis neck‐mounted accelerometers

Dairy cattle behavior classifications based on decision tree learning using 3‐axis neck‐mounted accelerometers

The utility of sensor technology to support reproductive management on dairy farms

Energy‐aware cluster‐based routing optimization for WSNs in the livestock industry

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