Classifying season long livestock grazing behavior with the use of a low-cost GPS and accelerometer

Brennan, Jameson R.; Johnson, Patricia S.; Olson, K. C.

doi:10.1016/j.compag.2020.105957

Cited by 46 publications

(38 citation statements)

References 24 publications

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“…Given that many operations require the deployment of multiple technologies simultaneously, often manufactured by different companies or are custom made ( Brennan et al, 2021 ; Tedeschi et al, 2021 ), effective data management and integration pose unique challenges to livestock production managers. In addition, researchers must consider the amount of data needed to capture the range of animal behaviors and generalize predictions to entire populations with sufficient frequency, quality, and precision.…”

Section: Precision Livestock Farmingmentioning

confidence: 99%

“…Advances have been made to utilize “off-the-shelf” GPS tracking devices and open-source hardware solutions to reduce the cost to track animals ( Knight et al, 2018 ; McGranahan et al, 2018 ; Karl and Sprinkle, 2019 ). In addition, GPS technology has been coupled with motion sensing technology such as 3-axis accelerometers that can help identify GPS locations associated with animal behaviors such as grazing, resting, and walking to better understand livestock behavior within extensive rangeland systems ( Augustine and Derner, 2013 ; Brennan et al, 2021 ; Sprinkle et al, 2021a ). For example, differences in grazing behavior can be used to identify differences in low residual feed intake vs. high residual feed intake cows, to ultimately select for animals that are better adapted to grazing rugged rangelands ( Sprinkle et al, 2021b ).…”

Section: Extensive Precision Operationsmentioning

confidence: 99%

“…The simplest example may include algorithms to detect and alert producers when animals are outside of pasture boundaries or remotely locate animals in areas where rough terrain or travel distance limits frequent opportunities for livestock managers to observe cattle welfare visually. 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%

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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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Section: Precision Livestock Farmingmentioning

confidence: 99%

Section: Extensive Precision Operationsmentioning

confidence: 99%

Section: Extensive Precision Operationsmentioning

confidence: 99%

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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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“…Based on their study, the best classifiers are RF and SVM. RF outperforms slightly SVM when it is trained on many data [22]. Tamura et al used 12bit and 3Daccelerometer sampled at 20Hz with Classification and Regression Tree (CART) algorithm to classify eating, rumination, and lying behaviors.…”

Section: Cowsmentioning

confidence: 99%

Farm Animals’ Behaviors and Welfare Analysis with IA Algorithms: A Review

Debauche¹,

Elmoulat²,

Mahmoudi³

et al. 2021

RIA

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Numerous bibliographic reviews related to the use of AI for the behavioral detection of farm animals exist, but they only focus on a particular type of animal. We believe that some techniques were used for some animals that could also be used for other types of animals. The application and comparison of these techniques between animal species are rarely done. In this paper, we propose a review of machine learning approaches used for the detection of farm animals’ behaviors such as lameness, grazing, rumination, and so on. The originality of this paper is matched classification in the midst of sensors and algorithms used for each animal category. First, we highlight the most implemented approaches for different categories of animals (cows, sheep, goats, pigs, horses, and chickens) to inspire researchers interested to conduct investigation and employ the methods we have evaluated and the results we have obtained in this study. Second, we describe the current trends in terms of technological development and new paradigms that will impact the AI research. Finally, we critically analyze what is done and we draw new pathways of research to advance our understanding of animal’s behaviors.

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“…Although some low-cost GPS tracking collars for livestock have been developed by readapting basic data loggers, they have a less reliable fix rate and fix schedule compared to commercial GPS [25]. Moreover, up to date, low-cost GPS have been tested only in USA [25][26][27][28] and Australian rangelands [29], and information about their performance is still unavailable in rough environments such as alpine pastures. As an alternative to GPS collars, animal positions can be mapped by direct observations [30].…”

Section: Introductionmentioning

confidence: 99%

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Classifying season long livestock grazing behavior with the use of a low-cost GPS and accelerometer

Cited by 46 publications

References 24 publications

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

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

Farm Animals’ Behaviors and Welfare Analysis with IA Algorithms: A Review

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