Creating Resilience in Pigs Through Artificial InteLligence (CuRly Pig Tail)

Research output: Chapter in Book/Report/Conference proceedingAbstractAcademic

Abstract

The pig provides a huge amount of health and welfare information by its behaviour and
appearance (e.g. lying, eating, skin colour, eye colour, hair coat and tail-posture). By careful
observation of this body language we believe it is possible to identify (early) signals of
discomfort, upcoming disease and undesired behaviour. By early detection of these signals
interventions can be carried out in an earlier stage than currently is done to restore health
and welfare of the pig herd. Good health and welfare is the foundation of high resilience in
animals, which makes them less vulnerable for disturbances (e.g. infections). For a farmer it
is, however, impossible to continuously monitor the body language and behaviour of every
pig on his or her farm. By using a combination of non-invasive techniques to collect signals
from the pigs and their housing environment (e.g. a camera and a water meter) the pigs can
be observed 24/7. By combining computer vision and pig knowledge using machine and deep
learning techniques, a non-invasive monitoring system can be designed. Deep learning is
the current state-of-the-art machine learning approach for computer vision that is especially
powerful in recognising and localising image content, e.g. the location of the body parts or
visible abnormalities thereof. Deep learning is based on large convolutional neural networks
and require a large amount of manually annotated training (image) data. Ultimately with this
approach the robustness of pig husbandry systems is increased due to better health and welfare
conditions for the animals. Additionally, our approach could even lead to a new design of pig
housing systems. Furthermore, it increases the job satisfaction of the farmer. Our ambition is to
develop advanced monitoring systems that allow to stop tail docking all together, so the curly
pig tail becomes once again a common phenomenon on pig farms. To achieve this ambition,
we will explore, co-develop and test non-invasive monitoring technologies for pig husbandry
Original languageEnglish
Title of host publicationProceedings of the 7th International Conference on the Assessment of Animal Welfare at Farm and Group level
EditorsIngrid C. De Jong, Paul Koene
Place of PublicationWageningen
PublisherWageningen Academic Publishers
Pages262-262
ISBN (Electronic)9789086868629
ISBN (Print)9789086863143
Publication statusPublished - 6 Sep 2017
EventWAFL 2017 - Hotel en Congrescentrum De Reehorst, Ede, Netherlands
Duration: 5 Sep 20178 Sep 2017

Conference

ConferenceWAFL 2017
CountryNetherlands
CityEde
Period5/09/178/09/17

Fingerprint

Artificial intelligence
Health
Farms
Computer vision
Monitoring
Water meters
Color
Job satisfaction
Learning systems
Skin
Animals
Cameras
Deep learning

Cite this

Timmerman, M., Kluivers-Poodt, M., Reimert, I., Vermeer, H. M., Barth, R., Kootstra, G. W., ... Lokhorst, C. (2017). Creating Resilience in Pigs Through Artificial InteLligence (CuRly Pig Tail). In I. C. De Jong, & P. Koene (Eds.), Proceedings of the 7th International Conference on the Assessment of Animal Welfare at Farm and Group level (pp. 262-262). Wageningen: Wageningen Academic Publishers.
Timmerman, M. ; Kluivers-Poodt, M. ; Reimert, I. ; Vermeer, H.M. ; Barth, R. ; Kootstra, G.W. ; van Riel, J.W. ; Lokhorst, C. / Creating Resilience in Pigs Through Artificial InteLligence (CuRly Pig Tail). Proceedings of the 7th International Conference on the Assessment of Animal Welfare at Farm and Group level. editor / Ingrid C. De Jong ; Paul Koene. Wageningen : Wageningen Academic Publishers, 2017. pp. 262-262
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title = "Creating Resilience in Pigs Through Artificial InteLligence (CuRly Pig Tail)",
abstract = "The pig provides a huge amount of health and welfare information by its behaviour andappearance (e.g. lying, eating, skin colour, eye colour, hair coat and tail-posture). By carefulobservation of this body language we believe it is possible to identify (early) signals ofdiscomfort, upcoming disease and undesired behaviour. By early detection of these signalsinterventions can be carried out in an earlier stage than currently is done to restore healthand welfare of the pig herd. Good health and welfare is the foundation of high resilience inanimals, which makes them less vulnerable for disturbances (e.g. infections). For a farmer itis, however, impossible to continuously monitor the body language and behaviour of everypig on his or her farm. By using a combination of non-invasive techniques to collect signalsfrom the pigs and their housing environment (e.g. a camera and a water meter) the pigs canbe observed 24/7. By combining computer vision and pig knowledge using machine and deeplearning techniques, a non-invasive monitoring system can be designed. Deep learning isthe current state-of-the-art machine learning approach for computer vision that is especiallypowerful in recognising and localising image content, e.g. the location of the body parts orvisible abnormalities thereof. Deep learning is based on large convolutional neural networksand require a large amount of manually annotated training (image) data. Ultimately with thisapproach the robustness of pig husbandry systems is increased due to better health and welfareconditions for the animals. Additionally, our approach could even lead to a new design of pighousing systems. Furthermore, it increases the job satisfaction of the farmer. Our ambition is todevelop advanced monitoring systems that allow to stop tail docking all together, so the curlypig tail becomes once again a common phenomenon on pig farms. To achieve this ambition,we will explore, co-develop and test non-invasive monitoring technologies for pig husbandry",
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Timmerman, M, Kluivers-Poodt, M, Reimert, I, Vermeer, HM, Barth, R, Kootstra, GW, van Riel, JW & Lokhorst, C 2017, Creating Resilience in Pigs Through Artificial InteLligence (CuRly Pig Tail). in IC De Jong & P Koene (eds), Proceedings of the 7th International Conference on the Assessment of Animal Welfare at Farm and Group level. Wageningen Academic Publishers, Wageningen, pp. 262-262, WAFL 2017, Ede, Netherlands, 5/09/17.

Creating Resilience in Pigs Through Artificial InteLligence (CuRly Pig Tail). / Timmerman, M.; Kluivers-Poodt, M.; Reimert, I.; Vermeer, H.M.; Barth, R.; Kootstra, G.W.; van Riel, J.W.; Lokhorst, C.

Proceedings of the 7th International Conference on the Assessment of Animal Welfare at Farm and Group level. ed. / Ingrid C. De Jong; Paul Koene. Wageningen : Wageningen Academic Publishers, 2017. p. 262-262.

Research output: Chapter in Book/Report/Conference proceedingAbstractAcademic

TY - CHAP

T1 - Creating Resilience in Pigs Through Artificial InteLligence (CuRly Pig Tail)

AU - Timmerman, M.

AU - Kluivers-Poodt, M.

AU - Reimert, I.

AU - Vermeer, H.M.

AU - Barth, R.

AU - Kootstra, G.W.

AU - van Riel, J.W.

AU - Lokhorst, C.

PY - 2017/9/6

Y1 - 2017/9/6

N2 - The pig provides a huge amount of health and welfare information by its behaviour andappearance (e.g. lying, eating, skin colour, eye colour, hair coat and tail-posture). By carefulobservation of this body language we believe it is possible to identify (early) signals ofdiscomfort, upcoming disease and undesired behaviour. By early detection of these signalsinterventions can be carried out in an earlier stage than currently is done to restore healthand welfare of the pig herd. Good health and welfare is the foundation of high resilience inanimals, which makes them less vulnerable for disturbances (e.g. infections). For a farmer itis, however, impossible to continuously monitor the body language and behaviour of everypig on his or her farm. By using a combination of non-invasive techniques to collect signalsfrom the pigs and their housing environment (e.g. a camera and a water meter) the pigs canbe observed 24/7. By combining computer vision and pig knowledge using machine and deeplearning techniques, a non-invasive monitoring system can be designed. Deep learning isthe current state-of-the-art machine learning approach for computer vision that is especiallypowerful in recognising and localising image content, e.g. the location of the body parts orvisible abnormalities thereof. Deep learning is based on large convolutional neural networksand require a large amount of manually annotated training (image) data. Ultimately with thisapproach the robustness of pig husbandry systems is increased due to better health and welfareconditions for the animals. Additionally, our approach could even lead to a new design of pighousing systems. Furthermore, it increases the job satisfaction of the farmer. Our ambition is todevelop advanced monitoring systems that allow to stop tail docking all together, so the curlypig tail becomes once again a common phenomenon on pig farms. To achieve this ambition,we will explore, co-develop and test non-invasive monitoring technologies for pig husbandry

AB - The pig provides a huge amount of health and welfare information by its behaviour andappearance (e.g. lying, eating, skin colour, eye colour, hair coat and tail-posture). By carefulobservation of this body language we believe it is possible to identify (early) signals ofdiscomfort, upcoming disease and undesired behaviour. By early detection of these signalsinterventions can be carried out in an earlier stage than currently is done to restore healthand welfare of the pig herd. Good health and welfare is the foundation of high resilience inanimals, which makes them less vulnerable for disturbances (e.g. infections). For a farmer itis, however, impossible to continuously monitor the body language and behaviour of everypig on his or her farm. By using a combination of non-invasive techniques to collect signalsfrom the pigs and their housing environment (e.g. a camera and a water meter) the pigs canbe observed 24/7. By combining computer vision and pig knowledge using machine and deeplearning techniques, a non-invasive monitoring system can be designed. Deep learning isthe current state-of-the-art machine learning approach for computer vision that is especiallypowerful in recognising and localising image content, e.g. the location of the body parts orvisible abnormalities thereof. Deep learning is based on large convolutional neural networksand require a large amount of manually annotated training (image) data. Ultimately with thisapproach the robustness of pig husbandry systems is increased due to better health and welfareconditions for the animals. Additionally, our approach could even lead to a new design of pighousing systems. Furthermore, it increases the job satisfaction of the farmer. Our ambition is todevelop advanced monitoring systems that allow to stop tail docking all together, so the curlypig tail becomes once again a common phenomenon on pig farms. To achieve this ambition,we will explore, co-develop and test non-invasive monitoring technologies for pig husbandry

M3 - Abstract

SN - 9789086863143

SP - 262

EP - 262

BT - Proceedings of the 7th International Conference on the Assessment of Animal Welfare at Farm and Group level

A2 - De Jong, Ingrid C.

A2 - Koene, Paul

PB - Wageningen Academic Publishers

CY - Wageningen

ER -

Timmerman M, Kluivers-Poodt M, Reimert I, Vermeer HM, Barth R, Kootstra GW et al. Creating Resilience in Pigs Through Artificial InteLligence (CuRly Pig Tail). In De Jong IC, Koene P, editors, Proceedings of the 7th International Conference on the Assessment of Animal Welfare at Farm and Group level. Wageningen: Wageningen Academic Publishers. 2017. p. 262-262