Wearable Wireless Biosensor Technology for Monitoring Cattle: A Review

Lee, Mingyung; Seo, Seongwon

doi:10.3390/ani11102779

Cited by 44 publications

(9 citation statements)

References 59 publications

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“…In the U.S., the National AMR Monitoring System (NARMS) was established in 1996 to monitor trends in AMR among foodborne bacteria. NARMS collects samples of bacteria from animals, retail meat, and humans, and tests them for resistance to a range of antibiotics [ 126 ].…”

Section: Amr Surveillance Programs In Food-producing Animalsmentioning

confidence: 99%

The Silent Threat: Antimicrobial-Resistant Pathogens in Food-Producing Animals and Their Impact on Public Health

Almansour,

Alhadlaq,

Alzahrani

et al. 2023

Microorganisms

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The emergence of antimicrobial resistance (AMR) is a global health problem without geographic boundaries. This increases the risk of complications and, thus, makes it harder to treat infections, which can result in higher healthcare costs and a greater number of deaths. Antimicrobials are often used to treat infections from pathogens in food-producing animals, making them a potential source of AMR. Overuse and misuse of these drugs in animal agriculture can lead to the development of AMR bacteria, which can then be transmitted to humans through contaminated food or direct contact. It is therefore essential to take multifaceted, comprehensive, and integrated measures, following the One Health approach. To address this issue, many countries have implemented regulations to limit antimicrobial use. To our knowledge, there are previous studies based on AMR in food-producing animals; however, this paper adds novelty related to the AMR pathogens in livestock, as we include the recent publications of this field worldwide. In this work, we aim to describe the most critical and high-risk AMR pathogens among food-producing animals, as a worldwide health problem. We also focus on the dissemination of AMR genes in livestock, as well as its consequences in animals and humans, and future strategies to tackle this threat.

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Section: Amr Surveillance Programs In Food-producing Animalsmentioning

confidence: 99%

The Silent Threat: Antimicrobial-Resistant Pathogens in Food-Producing Animals and Their Impact on Public Health

Almansour,

Alhadlaq,

Alzahrani

et al. 2023

Microorganisms

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“…Pahl et al 24 analyzed feeding characteristics recorded by weighing troughs and rumination time changes recorded by acoustic sensors based on the feeding characteristics before and after cow estrus. Lee et al 25 organized and analyzed the predicted physiological parameters and various data from a wearable wireless sensor system for cows. The sensors used in the system primarily focus on rumination behavior, including ear tags, collars, and reticulum boluses.…”

Section: Research Statusmentioning

confidence: 99%

UD-YOLOv5s: Recognition of cattle regurgitation behavior based on upper and lower jaw skeleton feature extraction

Gao,

Wang,

Wang

et al. 2023

J. Electron. Imag.

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Rumination plays a pivotal role in assessing the health status of ruminants. However, conventional contact devices such as ear tags and pressure sensors raise animal welfare concerns during rumination behavior detection. Deep learning offers a promising solution for non-contact rumination recognition by training neural networks on datasets. We introduce UD-YOLOv5s, an approach for bovine rumination recognition that incorporates jaw skeleton feature extraction techniques. Initially, a skeleton feature extraction method is proposed for the upper and lower jaws, employing skeleton heatmap descriptors and the Kalman filter algorithm. Subsequently, the UD-YOLOv5s method is developed for rumination recognition. To optimize the UD-YOLOv5s model, the traditional intersection over the union loss function is replaced with the generalized one. A self-built bovine rumination dataset is used to compare the performance of three deep learning techniques: mean shift algorithm, mask region-based convolutional neural network, and you only look once version 3 (YOLOv3). The results of the ablation experiment demonstrate that UD-YOLOv5s achieves impressive precision (98.25%), recall (97.75%), and a mean average precision of 93.43%. We conducted a generalization performance evaluation in a controlled experimental environment to ensure fairness, indicating that UD-YOLOv5s converges faster than other models while maintaining comparable recognition accuracy. Moreover, our work reveals that when convergence speed is equal, UD-YOLOv5s outperforms other models regarding recognition accuracy. These findings provide robust support for accurately identifying cattle rumination behavior, showcasing the potential of the UD-YOLOv5s method in advancing ruminant health assessment.

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“…AI and sensor technology can automate the tracking of individual animal feeding behaviors [ 18 , 19 ], including their time spent near feeding troughs, feeding frequency, and intake [ 20 ]. These data, once analyzed using AI algorithms, can facilitate early interventions.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation

Neethirajan

2023

Sensors

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This technical note critically evaluates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the swiftly evolving dairy livestock export industry. We focus on the novel application of the Internet of Things (IoT) in long-distance livestock transportation, particularly in livestock enumeration and identification for precise traceability. Technological advancements in identifying behavioral patterns in ‘shy feeder’ cows and real-time weight monitoring enhance the accuracy of long-haul livestock transportation. These innovations offer benefits such as improved animal welfare standards, reduced supply chain inaccuracies, and increased operational productivity, expanding market access and enhancing global competitiveness. However, these technologies present challenges, including individual animal customization, economic analysis, data security, privacy, technological adaptability, training, stakeholder engagement, and sustainability concerns. These challenges intertwine with broader ethical considerations around animal treatment, data misuse, and the environmental impacts. By providing a strategic framework for successful technology integration, we emphasize the importance of continuous adaptation and learning. This note underscores the potential of AI, IoT, and sensor technologies to shape the future of the dairy livestock export industry, contributing to a more sustainable and efficient global dairy sector.

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Wearable Wireless Biosensor Technology for Monitoring Cattle: A Review

Cited by 44 publications

References 59 publications

The Silent Threat: Antimicrobial-Resistant Pathogens in Food-Producing Animals and Their Impact on Public Health

The Silent Threat: Antimicrobial-Resistant Pathogens in Food-Producing Animals and Their Impact on Public Health

UD-YOLOv5s: Recognition of cattle regurgitation behavior based on upper and lower jaw skeleton feature extraction

Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation

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