Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Wurtz K

PROVIDER: S-EPMC6927615 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Wurtz Kaitlin K Camerlink Irene I D'Eath Richard B RB Fernández Alberto Peña AP Norton Tomas T Steibel Juan J Siegford Janice J

PloS one 20191223 12

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 behaviour ...[more]

PMID: 31869364

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Project description:Mankind directly controls the environment and lifestyles of several domestic species for purposes ranging from production and research to conservation and companionship. These environments and lifestyles may not offer these animals the best quality of life. Behaviour is a direct reflection of how the animal is coping with its environment. Behavioural indicators are thus among the preferred parameters to assess welfare. However, behavioural recording (usually from video) can be very time consuming and the accuracy and reliability of the output rely on the experience and background of the observers. The outburst of new video technology and computer image processing gives the basis for promising solutions. In this pilot study, we present a new prototype software able to automatically infer the behaviour of dogs housed in kennels from 3D visual data and through structured machine learning frameworks. Depth information acquired through 3D features, body part detection and training are the key elements that allow the machine to recognise postures, trajectories inside the kennel and patterns of movement that can be later labelled at convenience. The main innovation of the software is its ability to automatically cluster frequently observed temporal patterns of movement without any pre-set ethogram. Conversely, when common patterns are defined through training, a deviation from normal behaviour in time or between individuals could be assessed. The software accuracy in correctly detecting the dogs' behaviour was checked through a validation process. An automatic behaviour recognition system, independent from human subjectivity, could add scientific knowledge on animals' quality of life in confinement as well as saving time and resources. This 3D framework was designed to be invariant to the dog's shape and size and could be extended to farm, laboratory and zoo quadrupeds in artificial housing. The computer vision technique applied to this software is innovative in non-human animal behaviour science. Further improvements and validation are needed, and future applications and limitations are discussed.

Project description:In livestock species, the monolayer of epithelial cells covering the digestive mucosa plays an essential role for nutrition and gut barrier function. However, research on farm animal intestinal epithelium has been hampered by the lack of appropriate in vitro models. Over the past decade, methods to culture livestock intestinal organoids have been developed in pig, bovine, rabbit, horse, sheep and chicken. Gut organoids from farm animals are obtained by seeding tissue-derived intestinal epithelial stem cells in a 3-dimensional culture environment reproducing in vitro the stem cell niche. These organoids can be generated rapidly within days and are formed by a monolayer of polarized epithelial cells containing the diverse differentiated epithelial progeny, recapitulating the original structure and function of the native epithelium. The phenotype of intestinal organoids is stable in long-term culture and reflects characteristics of the digestive segment of origin. Farm animal intestinal organoids can be amplified in vitro, cryopreserved and used for multiple experiments, allowing an efficient reduction of the use of live animals for experimentation. Most of the studies using livestock intestinal organoids were used to investigate host-microbe interactions at the epithelial surface, mainly focused on enteric infections with viruses, bacteria or parasites. Numerous other applications of farm animal intestinal organoids include studies on nutrient absorption, genome editing and bioactive compounds screening relevant for agricultural, veterinary and biomedical sciences. Further improvements of the methods used to culture intestinal organoids from farm animals are required to replicate more closely the intestinal tissue complexity, including the presence of non-epithelial cell types and of the gut microbiota. Harmonization of the methods used to culture livestock intestinal organoids will also be required to increase the reproducibility of the results obtained in these models. In this review, we summarize the methods used to generate and cryopreserve intestinal organoids in farm animals, present their phenotypes and discuss current and future applications of this innovative culture system of the digestive epithelium.

Project description:BackgroundFarm work entails a heterogeneous mixture of exposures that vary considerably across farms and farmers. Farm work is associated with various health outcomes, both adverse and beneficial. One mechanism by which farming exposures can impact health is through the microbiome, including the indoor built environment microbiome. It is unknown how individual occupational exposures shape the microbial composition in workers' homes.ObjectivesWe investigated associations between farm work activities, including specific tasks and pesticide use, and the indoor microbiome in the homes of 468 male farmers.MethodsParticipants were licensed pesticide applicators, mostly farmers, enrolled in the Agricultural Lung Health Study from 2008-2011. Vacuumed dust from participants' bedrooms underwent whole-genome shotgun sequencing for indoor microbiome assessment. Using questionnaire data, we evaluated 6 farm work tasks (processing of either hay, silage, animal feed, fertilizer, or soy/grains, and cleaning grain bins) and 19 pesticide ingredients currently used in the past year, plus 7 persistent banned pesticide ingredients ever used.ResultsAll 6 work tasks were associated with increased within-sample microbial diversity, with a positive dose-response for the sum of tasks (p=0.001). All tasks were associated with altered overall microbial compositions (weighted UniFrac p=0.001) and with higher abundance of specific microbes, including soil-based microbes such as Haloterrigena . Among the 19 pesticides, only current use of glyphosate and past use of lindane were associated with increased within-sample diversity (p=0.02-0.04). Ten currently used pesticides and all 7 banned pesticides were associated with altered microbial composition (p=0.001-0.04). Six pesticides were associated with differential abundance of certain microbes.DiscussionSpecific farm activities and exposures can impact the dust microbiome inside homes. Our work suggests that occupational farm exposures could impact the health of workers and their families through modifying the indoor environment, specifically the microbial composition of house dust, offering possible future intervention targets.

Dataset Information

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

Publications

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

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