An automatic recognition framework for sow daily behaviours based on motion and image analyses

Yang, Aqing; Huang, Huasheng; Zheng, Bin; Li, Shimei; Gan, Haiming; Chen, Changxin; Yang, Xin‐She; Xue, Yong

doi:10.1016/j.biosystemseng.2020.01.016

Cited by 38 publications

(24 citation statements)

References 35 publications

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“…RFID systems have been used to monitor feeding patterns in pigs (Maselyne et al, 2016b;Adrion et al, 2018), turkeys (Tu et al, 2011) and laying hens (Li et al, 2017). Image analysis and binocular vision techniques have been developed to monitor feeding in pigs (Yang et al, 2020) and poultry (Xiao et al, 2019), while sensor-based systems can monitor feed intake in goats (Campos et al, 2019) and turkeys (Chagneau et al, 2006). Technologies at phase P2 also introduced the possibility to use 3D-vision to automatically assess reticulo-ruminal motility in cattle (Song et al, 2019).…”

Section: Technologies In Developmentmentioning

confidence: 99%

Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare

2021

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The rise in the demand for animal products due to demographic and dietary changes has exacerbated difficulties in addressing societal concerns related to the environment, human health, and animal welfare. As a response to this challenge, Precision Livestock Farming (PLF) technologies are being developed to monitor animal health and welfare parameters in a continuous and automated way, offering the opportunity to improve productivity and detect health issues at an early stage. However, ethical concerns have been raised regarding their potential to facilitate the management of production systems that are potentially harmful to animal welfare, or to impact the human-animal relationship and farmers' duty of care. Using the Five Domains Model (FDM) as a framework, the aim is to explore the potential of PLF to help address animal welfare and to discuss potential welfare benefits and risks of using such technology. A variety of technologies are identified and classified according to their type [sensors, bolus, image or sound based, Radio Frequency Identification (RFID)], their development stage, the species they apply to, and their potential impact on welfare. While PLF technologies have promising potential to reduce the occurrence of diseases and injuries in livestock farming systems, their current ability to help promote positive welfare states remains limited, as technologies with such potential generally remain at earlier development stages. This is likely due to the lack of evidence related to the validity of positive welfare indicators as well as challenges in technology adoption and development. Finally, the extent to which welfare can be improved will also strongly depend on whether management practices will be adapted to minimize negative consequences and maximize benefits to welfare.

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Section: Technologies In Developmentmentioning

confidence: 99%

Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare

2021

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“…In 18 of those 23 studies, the name or origin of the sensor was not provided; it was therefore impossible to identify its commercial availability or development stage. This applied to nine studies with camera-based technologies (29)(30)(31)(32)(33)(34)(35)(36)(37), three studies on load-cells [a drinker (38), a scale (39), and a force plate (40)], two on RFID (41,42), two on accelerometer (including one on accelerometer and microchip for body-temperature) (43,44), one study on microphone (45), and one on load cells with RFID (46). In the other five of those 23 studies, the origin or location of the herds used, or the origin of the Flow meters 2 0 2…”

Section: Literature Search On Validation Trialsmentioning

confidence: 99%

“…Measured traits were: feed intake, feeding and drinking frequency and duration, stress related to hunger or thirst, as well as nursing and suckling behavior. Sows' nursing behavior was monitored using cameras (37). The estimation of stress conditions related to hunger and thirst was assessed by vocalizations (90-92) using microphones and via skin temperature using thermal cameras (89), applying different stressors to the animals.…”

Section: Feeding and Drinking Behaviormentioning

confidence: 99%

“…Drinking patterns can be monitored using video analysis for evaluating visits to the drinker and contact time (94,95), and for distinguishing drinking from drinker-playing behavior (96). Cameras for the identification of behavior of sows were used for identifying feeding and drinking behavior in the farrowing crate (37,73,74), as well as in group-housed sows (97).…”

Section: Feeding and Drinking Behaviormentioning

confidence: 99%

“…However, none of the outstanding performance results for vision-based monitoring have been confirmed by external validation. Image-analysis has been used for assessing sows' postures, such as standing, lying, and sitting (73), evaluating lying patterns in group-housed pigs responding to thermal conditions of the pen (76, 80), detecting animals' location (32,84), distinguishing drinking and drinkerplaying behavior (96), identifying feeding behavior (37,73,74,97), recognizing aggression events (100-103) and tail biting (50), estimating body weight (36,118,119), and detecting African swine fever (31).…”

Section: Camera-based Technologiesmentioning

confidence: 99%

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A Systematic Review on Validated Precision Livestock Farming Technologies for Pig Production and Its Potential to Assess Animal Welfare

et al. 2021

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Several precision livestock farming (PLF) technologies, conceived for optimizing farming processes, are developed to detect the physical and behavioral changes of animals continuously and in real-time. The aim of this review was to explore the capacity of existing PLF technologies to contribute to the assessment of pig welfare. In a web search for commercially available PLF for pigs, 83 technologies were identified. A literature search was conducted, following systematic review guidelines (PRISMA), to identify studies on the validation of sensor technologies for assessing animal-based welfare indicators. Two validation levels were defined: internal (evaluation during system building within the same population that were used for system building) and external (evaluation on a different population than during system building). From 2,463 articles found, 111 were selected, which validated some PLF that could be applied to the assessment of animal-based welfare indicators of pigs (7% classified as external, and 93% as internal validation). From our list of commercially available PLF technologies, only 5% had been externally validated. The more often validated technologies were vision-based solutions (n = 45), followed by load-cells (n = 28; feeders and drinkers, force plates and scales), accelerometers (n = 14) and microphones (n = 14), thermal cameras (n = 10), photoelectric sensors (n = 5), radio-frequency identification (RFID) for tracking (n = 2), infrared thermometers (n = 1), and pyrometer (n = 1). Externally validated technologies were photoelectric sensors (n = 2), thermal cameras (n = 2), microphone (n = 1), load-cells (n = 1), RFID (n = 1), and pyrometer (n = 1). Measured traits included activity and posture-related behavior, feeding and drinking, other behavior, physical condition, and health. In conclusion, existing PLF technologies are potential tools for on-farm animal welfare assessment in pig production. However, validation studies are lacking for an important percentage of market available tools, and in particular research and development need to focus on identifying the feature candidates of the measures (e.g., deviations from diurnal pattern, threshold levels) that are valid signals of either negative or positive animal welfare. An important gap identified are the lack of technologies to assess affective states (both positive and negative states).

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Practice on Human Posture Based on OpenCV

2020

Advances in Intelligent Systems and Computing

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An automatic recognition framework for sow daily behaviours based on motion and image analyses

Cited by 38 publications

References 35 publications

Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare

Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare

A Systematic Review on Validated Precision Livestock Farming Technologies for Pig Production and Its Potential to Assess Animal Welfare

Practice on Human Posture Based on OpenCV

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