Setup, Test and Validation of a UHF RFID System for Monitoring Feeding Behaviour of Dairy Cows

Adrion, Felix; Keller, Markus; Bozzolini, Giulia Bianca; Umstätter, Christina

doi:10.3390/s20247035

Cited by 18 publications

(36 citation statements)

References 29 publications

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“…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

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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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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

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“…In addition, the RFID system inevitably has to consider the signal collision problem of multiple tags entering the reading area at the same time, and the nRF24LE1 integrates the carrier monitoring function, which can accurately monitor whether the current working channel has interference. erefore, the system chooses nRF24LE1 as the core chip, which can not only meet the requirements of low cost, low power consumption, high integration, and high performance, but also enhance the system's ability to prevent collisions and identify moving targets [13,14].…”

Section: Hardware Module Designmentioning

confidence: 99%

Design of Athlete’s Running Information Capture System in Space-Time Domain Based on Virtual Reality

Zhu

2022

Scientific Programming

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In order to improve the training effect of athletes, aiming at the problems of inaccurate information capture results, poor real-time performance of sports information capture, and inability to effectively suppress noise interference in traditional methods, an athlete space-time running information capture system based on virtual reality is designed. Establish the athlete’s human skeleton, obtain the athlete’s sports joint points, design the overall architecture of the athlete’s space-time running information capture system, and realize the whole link design of sports information capture through RF chip, infrared camera, sports data acquisition module, data transmission module, and human-computer interaction module. Based on virtual reality technology, a virtual reality environment is built to obtain the characteristic parameters of athletes’ sports posture in space-time domain, and the median filtering algorithm is used to filter the original signal to eliminate the impact of noise signal on athletes’ sports information capture. Finally, the activity of the motion region is detected, and the motion information is captured combined with the Gaussian mixture model. The experimental results show that the system designed in this paper has high accuracy and anti-interference and can realize the real-time capture of motion information.

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“…There were two main reasons: (1) the total number of samples in these two intervals was small, so even a small amount of false detection had a relatively large impact on the results; (2) the deformation of the tag led to an increase in false detection boxes that overlapped with the target but did not fully contain the numbers on the tag. There were only three ID tags in interval (7), which had fewer samples. When drawing its P-R curve, the accuracy and recall rate were both 100% with the confidence threshold set to 0.7, so its AP50 was 100%.…”

Section: Deformation and Brightness Robustnessmentioning

confidence: 99%

“…Radio frequency identification (RFID) is an individual identification method commonly used on large commercial dairy farms. RFID enables the recording of individual information, feeding information [7,8], and milk production [9,10] of dairy cows by reading tags attached to their bodies (usually ear tags) with wireless transmission technology. Compared with traditional methods, the reliability of information and the real-time information acquisition performance of RFID are improved.…”

Section: Introductionmentioning

confidence: 99%

A Cascaded Model Based on EfficientDet and YOLACT++ for Instance Segmentation of Cow Collar ID Tag in an Image

Zhao

Zhang

2021

Sensors

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In recent years, many imaging systems have been developed to monitor the physiological and behavioral status of dairy cows. However, most of these systems do not have the ability to identify individual cows because the systems need to cooperate with radio frequency identification (RFID) to collect information about individual animals. The distance at which RFID can identify a target is limited, and matching the identified targets in a scenario of multitarget images is difficult. To solve the above problems, we constructed a cascaded method based on cascaded deep learning models, to detect and segment a cow collar ID tag in an image. First, EfficientDet-D4 was used to detect the ID tag area of the image, and then, YOLACT++ was used to segment the area of the tag to realize the accurate segmentation of the ID tag when the collar area accounts for a small proportion of the image. In total, 938 and 406 images of cows with collar ID tags, which were collected at Coldstream Research Dairy Farm, University of Kentucky, USA, in August 2016, were used to train and test the two models, respectively. The results showed that the average precision of the EfficientDet-D4 model reached 96.5% when the intersection over union (IoU) was set to 0.5, and the average precision of the YOLACT++ model reached 100% when the IoU was set to 0.75. The overall accuracy of the cascaded model was 96.5%, and the processing time of a single frame image was 1.92 s. The performance of the cascaded model proposed in this paper is better than that of the common instance segmentation models, and it is robust to changes in brightness, deformation, and interference around the tag.

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Setup, Test and Validation of a UHF RFID System for Monitoring Feeding Behaviour of Dairy Cows

Cited by 18 publications

References 29 publications

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

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

Design of Athlete’s Running Information Capture System in Space-Time Domain Based on Virtual Reality

A Cascaded Model Based on EfficientDet and YOLACT++ for Instance Segmentation of Cow Collar ID Tag in an Image

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