[PhD’s corner]Maëva Durand : Integrated automated on real-time welfare and health assessment of gestating sows using heterogeneous data for precision feeding

Maëva is one of the #DigitAg cofunded PhDs

 

Integrated automated on real-time welfare and health assessment of gestating sows using heterogeneous data for precision feeding

  • Start Date: 1st October 2020
  • University: AgroCampus Ouest
  • PhD School: Ecole Doctorale Écologie, Géosciences, Agronomie, Alimentation (EGAAL)
  • Field(s): Agriculture, Animal Sciences
  • Doctoral Thesis Advisor: Jean-Yves Dourmand, Pegase, Inrae
  • Co-supervisors : Charlotte Gaillard, Pegase, Inrae and Christine Largouët, Irisa, Inria
  • Funding: #DigitAg- Inrae
  • #DigitAg: Axis 2: Innovation in digital agriculture, Axis 5 : Data Mining, Data Analysis and Knowledge Discovery – Challenge 2: Digital solutions to optimize the genotype in changing production systems and markets, Challenge 4: ICT and sustainable animal production

Keywords: behaviour, welfare, precision breeding, technology, sows

Abstract: Evaluating sows’ behaviour allows the early detection of health or welfare problems and the quantification of their physical activity, which is major factor affecting their energy requirement. In practice, the continuous observation of each animal by the breeder is impossible and only intermittent observations are performed, often only once a day. New technologies have been developed like the accelerometers measuring the activity of each animal and its behaviour as well as software analysing animals’ vocalizations. Video analysis also offers the possibility to study social interactions between animals. Feeding and drinking behaviours may also be collected by feed and water dispensers. These behavioural data coupled with production data (feed intake, body weight and backfat thickness) should allow real-time estimation of the welfare of each animal, estimated through different criteria, and anticipate the occurrence of health problems in a non-invasive manner. The innovative issue is to describe the behavioural models using timed automata. The learning of timed automata is a rather new area of research. Their interest lies in time representation, the efficiency of exploration (using model-checking techniques) and the explainability of the models for predictive and monitoring tasks. The objectives of this thesis are therefore, in a first step, (i) to collect behavioural and production data in different situations (non-stressful vs. stressful), (ii) to learn and model the behaviours with timed automata describing gestating sows and the relationships between them in different situations (iii) to set up alerts and actions system to improve welfare, and (iv) to test in the experimental farm the capacity of this system to improve welfare and health.

Contact: durand.maeva22@gmail.com – Phone: 06 89 06 76 26