Use of an ear-tag accelerometer and a radio-frequency identification (RFID) system for monitoring the licking behaviour in grazing cattle

Simanungkalit, Gamaliel; Barwick, Jamie; Cowley, Frances; Dawson, Bradley; Dobos, Robin; Hegarty, Roger

doi:10.1016/j.applanim.2021.105491

Cited by 13 publications

(6 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Simanungkalit et al [35], the battery life was a little bit longer. Four 3-dimensional accelerometers with a sampling rate of 25 Hz were used in their study.…”

Section: Accelerometer-based Monitoring Systemsmentioning

confidence: 98%

“…Simanungkalit et al [35] investigated the capacity of an ear tag accelerometer to detect licking behaviour at a block supplement in grazing cattle and validated the length of individual licking state predicted by an accelerometer and a system radio frequency identification system (RFID). Four Angus steers were fitted with an ear tag carrying a three-axis accelerometer.…”

Section: Accelerometer-based Monitoring Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Cow Behavioural Activities in Extensive Farms: Challenges of Adopting Automatic Monitoring Systems

Mancuso

Castagnolo

Porto

2023

Sensors

View full text Add to dashboard Cite

Animal welfare is becoming an increasingly important requirement in the livestock sector to improve, and therefore raise, the quality and healthiness of food production. By monitoring the behaviour of the animals, such as feeding, rumination, walking, and lying, it is possible to understand their physical and psychological status. Precision Livestock Farming (PLF) tools offer a good solution to assist the farmer in managing the herd, overcoming the limits of human control, and to react early in the case of animal health issues. The purpose of this review is to highlight a key concern that occurs in the design and validation of IoT-based systems created for monitoring grazing cows in extensive agricultural systems, since they have many more, and more complicated, problems than indoor farms. In this context, the most common concerns are related to the battery life of the devices, the sampling frequency to be used for data collection, the need for adequate service connection coverage and transmission range, the computational site, and the performance of the algorithm embedded in IoT-systems in terms of computational cost.

show abstract

“…In Simanungkalit et al [35], the battery life was a little bit longer. Four 3-dimensional accelerometers with a sampling rate of 25 Hz were used in their study.…”

Section: Accelerometer-based Monitoring Systemsmentioning

confidence: 98%

Section: Accelerometer-based Monitoring Systemsmentioning

confidence: 99%

Cow Behavioural Activities in Extensive Farms: Challenges of Adopting Automatic Monitoring Systems

Mancuso

Castagnolo

Porto

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…RFID systems have been used to detect feeding behaviour [118][119][120], drinking behaviour [121,122] and supplement intake [123] based on proximity to the feed bunk, water point and mineral lick block, respectively. Toaf et al [124] found that an RFID-based ear tag recording of brush proximity was not yet a reliable representation of grooming behaviour as some animals spent a relatively large portion of time standing idle close to the brush, resulting in false positives.…”

Section: Radio Frequency Identification (Rfid)mentioning

confidence: 99%

Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review

Hofstra

Roelofs

Rutter

et al. 2022

Dairy

View full text Add to dashboard Cite

Several studies have suggested that precision livestock farming (PLF) is a useful tool for animal welfare management and assessment. Location, posture and movement of an individual are key elements in identifying the animal and recording its behaviour. Currently, multiple technologies are available for automated monitoring of the location of individual animals, ranging from Global Navigation Satellite Systems (GNSS) to ultra-wideband (UWB), RFID, wireless sensor networks (WSN) and even computer vision. These techniques and developments all yield potential to manage and assess animal welfare, but also have their constraints, such as range and accuracy. Combining sensors such as accelerometers with any location determining technique into a sensor fusion system can give more detailed information on the individual cow, achieving an even more reliable and accurate indication of animal welfare. We conclude that location systems are a promising approach to determining animal welfare, especially when applied in conjunction with additional sensors, but additional research focused on the use of technology in animal welfare monitoring is needed.

show abstract

“…PLF approaches provide the opportunity for continuous and objective monitoring of individual animals and may be used for the assessment of animal health and welfare, productivity, sustainability, and overall farm management [ 5 , 6 , 7 , 8 ]. Data collected by accelerometers have been used to classify livestock behaviours such as grazing, ruminating, and walking [ 9 , 10 , 11 , 12 , 13 ], as well as predict biting and chewing rates [ 14 , 15 ]. Accelerometer data can also be used to quantify activity levels, representing the cumulative forces measured over a specific time window.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data

Hu,

Reverter,

Arablouei

et al. 2024

Animals

View full text Add to dashboard Cite

In this study, we equip two breeds of cattle located in tropical and temperate climates with smart ear tags containing triaxial accelerometers to measure their activity levels across different time periods. We produce activity profiles when measured by each of four statistical features, the mean, median, standard deviation, and median absolute deviation of the Euclidean norm of either unfiltered or high-pass-filtered accelerometer readings over five-minute windows. We then aggregate the values from the 5 min windows into hourly or daily (24 h) totals to produce activity profiles for animals kept in each of the test environments. To gain a better understanding of the variation between the peak and nadir activity levels within a 24 h period, we divide each day into multiple equal-length intervals, which can range from 2 to 96 intervals. We then calculate a statistical measure, called daily differential activity (DDA), by computing the differences in feature values for each interval pair. Our findings demonstrate that patterns within the activity profile are more clearly visualised from readings that have been subject to high-pass filtering and that the median of the acceleration vector norm is the most reliable feature for characterising activity and calculating the DDA measure. The underlying causes for these differences remain elusive and is likely attributable to environmental factors, cattle breeds, or management practices. Activity profiles produced from the standard deviation (a feature routinely applied to the quantification of activity level) showed less uniformity between animals and larger variation in values overall. Assessing activity using ear tag accelerometers holds promise for monitoring animal health and welfare. However, optimal results may only be attainable when true diurnal patterns are detected and accounted for.

show abstract

Use of an ear-tag accelerometer and a radio-frequency identification (RFID) system for monitoring the licking behaviour in grazing cattle

Cited by 13 publications

References 39 publications

Cow Behavioural Activities in Extensive Farms: Challenges of Adopting Automatic Monitoring Systems

Cow Behavioural Activities in Extensive Farms: Challenges of Adopting Automatic Monitoring Systems

Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review

Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data

Contact Info

Product

Resources

About