Sensor based disease detection: A case study using accelerometers to recognize symptoms of Bovine Ephemeral Fever

Tobin, Colin; Bailey, Derek W.; Trotter, Mark; O’Connor, Lauren R.

doi:10.1016/j.compag.2020.105605

Cited by 25 publications

(26 citation statements)

References 15 publications

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“…Arcidiacono [36] et al analyzed the behavior data of cows collected with accelerometers and determined the threshold for determining cows in estrus. Tobin [37] et al used accelerometers to detect the behavior data of cows infected with rinderpest and found that they were different from normal cows. Foldager [38] uses accelerometers and radio frequency identification (RFID) prototype devices installed on the collar to collect data, and detects the feed intake and feeding time of dairy cows, with an accuracy of more than 85%.…”

Section: B Comparison Of Test Resultsmentioning

confidence: 99%

Real-Time Behavioral Recognition in Dairy Cows Based on Geomagnetism and Acceleration Information

et al. 2021

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The feeding, ruminating(standing, lying down), running, being still (standing, lying down), head-shaking, drinking, and walking behaviors of dairy cows can reflect their health status. In this study, a multi-sensor was used to collect data of cow's multi-behaviors for research on behavior recognition. Firstly, a collar style data acquisition system was designed using geomagnetic and acceleration sensors to collect the behavioral data of dairy cows during their daily activities. Secondly, the dairy cow behavioral recognition fusion model based on K-Nearest-Neighbors (KNN) and Random Forest (RF) models were used for behavior classification. To verify the accuracy of the fusion model, the algorithms of KNN, RF, Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Learning Vector Quantization (LVQ) were introduced for comparative recognition experiments among different algorithms. The KNN-RF fusion model had the highest average recognition accuracy of 98.51%, followed by the KNN model with an average recognition accuracy of 95.37%, and the LVQ model had the lowest average recognition accuracy of 80.81%. For the recognition and verification of each behavior, the KNN-RF fusion model had the most obvious improvement in the recognition of dairy cow feeding behavior, with a recognition accuracy of 99.34%, followed by the KNN model with a recognition accuracy of 95.07%. All six models had the lowest recognition accuracy for cow head-shaking behavior: a recognition accuracy of 89.11% with the KNN-RF model followed by the RF model with a recognition accuracy of 85.14%. The system can quickly and continuously collect cow behavior information, accurately recognize individual behaviors, and provide a scientific basis for the optimal design and efficient management of digital facilities and equipment for dairy cows.

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Section: B Comparison Of Test Resultsmentioning

confidence: 99%

Real-Time Behavioral Recognition in Dairy Cows Based on Geomagnetism and Acceleration Information

et al. 2021

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“…More advanced algorithms have been used with GPS and accelerometer sensors to accurately predict livestock behavior and calculate metrics such as individual daily time spent grazing ( Brennan et al, 2021 ). Variability in movements and behavior associated with GPS-tracked livestock can be an effective way to monitor livestock welfare concerns such as water failure, disease detection, or changes in behavior linked to distress or parturition ( Tedeschi and Menendez, 2020 ; Tobin et al, 2020 , 2021 ; Fogarty et al, 2021 ).…”

Section: Extensive Precision Operationsmentioning

confidence: 99%

ASAS–NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production

Menendez

Brennan

Gaillard

et al. 2022

Journal of Animal Science

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Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSM). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals, while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in, more challenging and harsh, extensive systems it is likely that confined operations will benefit from required advances in precision technology and PSM, ultimately strengthening the benefits from precision technology to achieve short-and long-term goals.

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“…Cattle and sheep with experimentally induced rabies had the distinct behavioral signs of infection with increased excitability, aggression, head tremors, and vocalizations (Hudson et al, 1996). Tobin et al (2020) conducted a proof-of-concept study that showed that accelerometers had the potential to detect diseases such as bovine ephemeral fever in a small pasture setting. Heifer activity dropped during the 24-h period prior to diagnosis of bovine ephemeral fever.…”

Section: Remotely Monitoring For Livestock Diseasementioning

confidence: 99%

Opportunities to Apply Precision Livestock Management on Rangelands

Bailey

Trotter

Tobin

et al. 2021

Front. Sustain. Food Syst.

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Precision livestock management has become a new field of study as the result of recent advancements in real-time global positioning system (GPS) tracking, accelerometer and other sensor technologies. Real-time tracking and accelerometer monitoring has the potential to remotely detect livestock disease, animal well-being and grazing distribution issues and notify ranchers and graziers so that they can respond as soon as possible. On-going research has shown that accelerometers can remotely monitor livestock behavior and detect activity changes that are associated with disease and parturition. GPS tracking can also detect parturition by monitoring the distance between a ewe and the remainder of the flock. Tracking also has the potential to detect water system failures. Combinations of GPS tracking and accelerometer monitoring may be more accurate than either device used by itself. Real-time GPS tracking can identify when livestock congregate in environmental sensitive areas which may allow managers the chance to respond before resource degradation occurs. Identification of genetic markers associated with terrain use, decreased cost of GPS tracking and novel tracking data processing should facilitate development of tools needed for genetic selection for cattle grazing distribution. Precision livestock management has potential to improve welfare of livestock grazing rangelands and forested lands, reduce labor costs and improve ranch profitability and improve the condition and sustainability of riparian areas and other environmental sensitive areas on grazing lands around the world.

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Sensor based disease detection: A case study using accelerometers to recognize symptoms of Bovine Ephemeral Fever

Cited by 25 publications

References 15 publications

Real-Time Behavioral Recognition in Dairy Cows Based on Geomagnetism and Acceleration Information

Real-Time Behavioral Recognition in Dairy Cows Based on Geomagnetism and Acceleration Information

ASAS–NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production

Opportunities to Apply Precision Livestock Management on Rangelands

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