Use of a real-time location system to detect cows in distinct functional areas within a barn

Chapa, J.M.; Lidauer, Laura; Steininger, A.; Öhlschuster, M.; Potrusil, T.; Sigler, M.; Auer, Wolfgang; Azizzadeh, Mohammad; Drillich, M.; Iwersen, M.

doi:10.3168/jdsc.2020-0050

Cited by 7 publications

(3 citation statements)

References 31 publications

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“…In addition to improving the algorithm, adding information by wearable devices is also helpful to increase the monitoring precision. The devices, including motion sensors, magnetometers, gyroscopes, and GPS receivers, have been widely used to monitor behavioral patterns in large farming environments (i.e., pastures and barns) ( Chapa et al, 2021 ; Li et al, 2021 ; Nikodem, 2021 ; Perisho and Hajnal, 2021 ; dos Reis et al, 2021 ). Specifically, with the use of tracking collar wore by pastured livestock, grazing behaviors were successfully identified for cattle ( Brennan et al, 2021 ) and sheep ( dos Reis et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…The spatial resolution for outdoor studies was further improved to centimeter-level by coupling sensor collars with signal receivers deployed around the farm ( Li et al, 2021 ). In group-housed scenarios, Smartbow (Weibern, Austria), a commercialized ear-tag sensor system, also demonstrated promising results in monitoring complex interactions on reproduction traits in swine cohorts ( Perisho and Hajnal, 2021 ) and feeding behaviors of cows ( Chapa et al, 2021 ). In conclusion, by coupling the VTag algorithm and the described improvement, the automation of the assessment system is expected to monitor more complex farm settings.…”

Section: Discussionmentioning

confidence: 99%

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VTag: a semi-supervised pipeline for tracking pig activity with a single top-view camera

Chen

Morota

Lee

et al. 2022

Journal of Animal Science

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Precision livestock farming has become an important research focus with the rising demand of meat production in the swine industry. Currently, the farming practice is widely conducted by the technology of computer vision (CV), which automates monitoring pig activity solely based on video recordings. Automation is fulfilled by deriving imagery features that can guide CV systems to recognize animals’ body contours, positions, and behavioral categories. Nevertheless, the performance of the CV systems is sensitive to the quality of imagery features. When the CV system is deployed in a variable environment, its performance may decrease as the features are not generalized enough under different illumination conditions. Moreover, most CV systems are established by supervised learning, in which intensive effort in labeling ground truths for the training process is required. Hence, a semi-supervised pipeline, VTag, is developed in this study. The pipeline focuses on long-term tracking of pig activity without requesting any pre-labeled video but a few human supervisions to build a CV system. The pipeline can be rapidly deployed as only one top-view RGB camera is needed for the tracking task. Additionally, the pipeline was released as a software tool with a friendly graphical interface available to general users. Among the presented datasets, the average tracking error was 17.99 centimeters. Besides, with the prediction results, the pig moving distance per unit time can be estimated for activity studies. Lastly, as the motion is monitored, a heat map showing spatial hot spots visited by the pigs can be useful guidance for farming management. The presented pipeline saves massive laborious work in preparing training dataset. The rapid deployment of the tracking system paves the way for pig behavior monitoring.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

VTag: a semi-supervised pipeline for tracking pig activity with a single top-view camera

Chen

Morota

Lee

et al. 2022

Journal of Animal Science

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“…These techniques, ranging from global positioning systems (GPS) to ultra-wideband (UWB), Bluetooth and even computer vision, yield the potential to manage and assess animal welfare. The location of a cow in a functional area, for instance, is closely related to the activity of the animal, e.g., feeding, lying, and drinking (basic needs) and thus location can be used to infer its behavioural budget [23,24]. Deviations from predetermined behavioural patterns in turn might be an indicator of disease or compromised welfare [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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

Hofstra

Roelofs

Rutter

et al. 2022

Dairy

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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.

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Efficient Estimation of Cow’s Location Using Machine Learning Based on Sensor Data

Sawada¹,

Uchino²,

Martono³

et al. 2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Use of a real-time location system to detect cows in distinct functional areas within a barn

Cited by 7 publications

References 31 publications

VTag: a semi-supervised pipeline for tracking pig activity with a single top-view camera

VTag: a semi-supervised pipeline for tracking pig activity with a single top-view camera

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

Efficient Estimation of Cow’s Location Using Machine Learning Based on Sensor Data

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