PLF technology for monitoring abnormal behaviour in pigs

This technology can improve the efficiency and welfare in modern pig production facilities
Abstract: 

Tail biting is a major welfare, economical and ethical challenge faced during pig production. Monitoring the risks of damaging tail biting will be an important way to reduce its occurrence on farm and give farmers greater confidence to raise pigs with undocked tails. This technology aims to provide a data-driven decision support tool that will advise farmers on how to reduce tail biting occurrences based on mainly behaviour-based variables collected remotely during production.

Innovation description: 

Tail biting is a major welfare, economical and ethical challenge faced during pig production. Monitoring the risks of damaging tail biting will be an important way to reduce its occurrence on farm and give farmers greater confidence to raise pigs with undocked tails. This technology aims to provide a data-driven decision support tool that will advise farmers on how to reduce tail biting occurrences based on mainly behaviour-based variables collected remotely during production.

Problem addressed: 

Tail biting is a major welfare, economical and ethical challenge faced during pig production. Monitoring the risks of damaging tail biting will be an important way to reduce its occurrence on farm and give farmers greater confidence to raise pigs with undocked tails. 

Solution offered: 

This technology aims to provide a data-driven decision support tool that will advise farmers on how to reduce tail biting occurrences based on mainly behaviour-based variables collected remotely during production.

Innovation stage: 

We are currently at TRL 6

SDG: 
12 – Responsible consumption and production
ICT domain: 
3.Proximal sensors
4.Big data technologies/Artificial Intelligence/Machine Learning
AGRI domain: 
7.Improve animal health and welfare
Technological Readiness Level (TRL): 
Development phase (TRL 4,5 & 6)
Project name : 
An ICT-based real-time advisory tool to minimise tail biting in fattening pigs
Project acronym: 
TailBiteAdvice
Call: 
2019 – Cofund Call on ICT-enabled agri-food systems
Project duration : 
Tuesday, 1 February, 2022 to Wednesday, 31 July, 2024
Institution: 
KU Leuven
Role in the project : 
Coordinator
Members of the research groups: 
Tomas Norton and Dong Liu
Contact e-mails: 
tomas.norton@kuleuven.be
Funders: 
Flanders Innovation & Entrepreneurship (VLAIO)
References: 
Liu, D., Parmiggiani, A., Psota, E., Fitzgerald, R. and Norton, T., 2023. Where's your head at? Detecting the orientation and position of pigs with rotated bounding boxes. Computers and Electronics in Agriculture, 212, p.108099.
Parmiggiani, A., Liu, D., Psota, E., Fitzgerald, R. and Norton, T., 2023. Don’t get lost in the crowd: Graph convolutional network for online animal tracking in dense groups. Computers and Electronics in Agriculture, 212, p.108038.
Liu, D., Parmiggiani, A., Norton, T., 2023.MT-SRnet: A Transferable Multi-Task Super-Resolution Network for Addressing Pig Keypoints, Mask, and Posture (in preparation) 6. Mid-term work package report submitted
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