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
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 language | English |
|---|---|
| Title of host publication | Proceedings of the 7th International Conference on the Assessment of Animal Welfare at Farm and Group level |
| Editors | Ingrid C. De Jong, Paul Koene |
| Place of Publication | Wageningen |
| Publisher | Wageningen Academic Publishers |
| Pages | 111-111 |
| ISBN (Electronic) | 9789086868629 |
| ISBN (Print) | 9789086863143 |
| Publication status | Published - 2017 |
| Event | WAFL 2017 - Hotel en Congrescentrum De Reehorst, Ede, Netherlands Duration: 5 Sept 2017 → 8 Sept 2017 |
Conference/symposium
| Conference/symposium | WAFL 2017 |
|---|---|
| Country/Territory | Netherlands |
| City | Ede |
| Period | 5/09/17 → 8/09/17 |