Image analysis to refine measurements of dairy cow behaviour from a real-time location system

Meunier, Bruno; Pradel, Philippe; Sloth, Karen Helle; Cirie, Carole; Delval, Eric; Mialon, Marie M.; Veissier, Isabelle

doi:10.1016/j.biosystemseng.2017.08.019

Cited by 58 publications

(39 citation statements)

References 28 publications

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“…Several studies have employed sensor systems to monitor the location of dairy cattle using different methods including GPS for pasture-based animals (Williams et al, 2016) and various real-time location system (RTLS) radio frequency-based technologies for indoor sensing (Gygax et al, 2007;Alarifi et al, 2016;Shane et al, 2016;Meunier et al, 2017). Although validated for use on farms (e.g., Tullo et al, 2016), very few studies have examined at the application of these systems in dairy management or combined RTLS location data with activity data recorded from accelerometers.…”

Section: Introductionmentioning

confidence: 99%

Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle

Barker

Diosdado

Codling

et al. 2018

Journal of Dairy Science

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Section: Introductionmentioning

confidence: 99%

Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle

Barker

Diosdado

Codling

et al. 2018

Journal of Dairy Science

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“…Animal behavior such as, walking, foraging, standing, lying, can be derived from high-frequency GPS. This approach allows tracking cows in open and forested habitat [37], [38], [39]. Spatiotemporal patterns of cattle grazing were studied in four annual grassland pastures in North America, differing mainly in tree canopy cover.…”

Section: Foraging Ecologymentioning

confidence: 99%

Internet of Things for evaluating foraging and feeding behavior of cattle on grassland-based farming systems: concepts and review of sensor technologies

Álvarez¹,

Valdés²,

Perez-Teruel³

2019

Proceedings of ISP RAS

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In this paper, we give an overview of the movement, foraging and feeding ecology as well as sensors technologies that could be embedded into an IoT-based platform for Precision Livestock Farming (PLF). A total of 43 peer-reviewed journal papers indexed by Web of Science were surveyed. Firstly, sensors technologies (e.g., RFID, GPS, or Accelerometer) used by the authors of each paper were identified. Then, papers were classified according to their applicability to ecological studies in the fields of foraging and feeding behavior.

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“…By analyzing image/video and scene information of the drinking area, the location and drinking behavior of cows was characterized [23]. Based on the top view of a bull barn, Meunier [24] divided the barn layout into eating, walkway, resting, and milking areas. Positional information for dairy cows was obtained with a real-time location system, which generated the real-time behavioral status of cows, and the time period of each individual behavior such as feeding and resting, and interactive behaviors.…”

Section: Introductionmentioning

confidence: 99%

A Machine Vision-Based Method for Monitoring Scene-Interactive Behaviors of Dairy Calf

Guo

Chai

2020

Animals

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Requirements for animal and dairy products are increasing gradually in emerging economic bodies. However, it is critical and challenging to maintain the health and welfare of the increasing population of dairy cattle, especially the dairy calf (up to 20% mortality in China). Animal behaviors reflect considerable information and are used to estimate animal health and welfare. In recent years, machine vision-based methods have been applied to monitor animal behaviors worldwide. Collected image or video information containing animal behaviors can be analyzed with computer languages to estimate animal welfare or health indicators. In this proposed study, a new deep learning method (i.e., an integration of background-subtraction and inter-frame difference) was developed for automatically recognizing dairy calf scene-interactive behaviors (e.g., entering or leaving the resting area, and stationary and turning behaviors in the inlet and outlet area of the resting area) based on computer vision-based technology. Results show that the recognition success rates for the calf’s science-interactive behaviors of pen entering, pen leaving, staying (standing or laying static behavior), and turning were 94.38%, 92.86%, 96.85%, and 93.51%, respectively. The recognition success rates for feeding and drinking were 79.69% and 81.73%, respectively. This newly developed method provides a basis for inventing evaluation tools to monitor calves’ health and welfare on dairy farms.

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Image analysis to refine measurements of dairy cow behaviour from a real-time location system

Cited by 58 publications

References 28 publications

Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle

Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle

Internet of Things for evaluating foraging and feeding behavior of cattle on grassland-based farming systems: concepts and review of sensor technologies

A Machine Vision-Based Method for Monitoring Scene-Interactive Behaviors of Dairy Calf

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