Recognition and drinking behaviour analysis of individual pigs based on machine vision

Zhu, Weixing; Guo, Yizheng; Jiao, Pengpeng; Ma, Chenguang; Chen, Chen

doi:10.1016/j.livsci.2017.09.003

Cited by 44 publications

(30 citation statements)

References 24 publications

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“…They were able to achieve recognition accuracy of 92%. Zhu et al implemented machine vision to recognize drinking behaviour [87] of individual pigs within a drinking zone by colour moment features and the extraction of geometric features such as area and object contour perimeters. This method of image processing allowed for individual pig recognition without the need for individual marking of animals.…”

Section: Resultsmentioning

confidence: 99%

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review

et al. 2019

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Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced.

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Section: Resultsmentioning

confidence: 99%

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review

et al. 2019

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“…RFID systems were also validated for registering feeding and drinking patterns of individual growing-finishing pigs (58,86,93). 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%

“…Internal validation of the vision-based technologies in many cases reported very promising results with accuracy above 95% [e.g., (30,33,58,73,74,93,95,97,107,(145)(146)(147)]. However, none of the outstanding performance results for vision-based monitoring have been confirmed by external validation.…”

Section: Camera-based Technologiesmentioning

confidence: 99%

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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“…The spread of infectious diseases is a huge threat to livestock farming. In order to detect the early symptoms of swine disease in time, people need to conduct effective monitoring of the group-housed pigs suffering from disease [1,2] through methods such as temperature measurement by infrared ray, detection of cough [3,4], calculation of the amount of drinking water [5], eating and drinking behavior recognition [6,7], and behavioral change measurement [8]. In this process, one of the most important basic tasks is to distinguish different pigs and identify pigs with abnormal behavior.…”

Section: Introductionmentioning

confidence: 99%

Weber Texture Local Descriptor for Identification of Group-Housed Pigs

Huang

Zhu

et al. 2020

Sensors

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The individual identification of group-housed pigs plays an important role in breeding process management and individual behavior analysis. Recently, livestock identification methods based on the side view or face image have strict requirements on the position and posture of livestock, which poses a challenge for the application of the monitoring scene of group-housed pigs. To address the issue above, a Weber texture local descriptor (WTLD) is proposed for the identification of group-housed pigs by extracting the local features of back hair, skin texture, spots, and so on. By calculating the differential excitation and multi-directional information of pixels, the local structure features of the main direction are fused to enhance the description ability of features. The experimental results show that the proposed WTLD achieves higher recognition rates with a lower feature dimension. This method can identify pig individuals with different positions and postures in the pig house. Without limitations on pig movement, this method can facilitate the identification of individual pigs with greater convenience and universality.

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Recognition and drinking behaviour analysis of individual pigs based on machine vision

Cited by 44 publications

References 24 publications

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review

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

Weber Texture Local Descriptor for Identification of Group-Housed Pigs

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