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The use and analysis of data acquired in dairy farming is a challenge both for data science and for animal science. Its goal is to improve farming conditions (health, welfare and environment) as well as farmers’ income. Nowadays, animals are monitored by multiple sensors giving a wealth of heterogeneous data (ex. temperature, weight, milk composition). Current techniques used by animal scientists focus mostly on mono-sensor approaches. The dynamic combination of several sensors could provide new services and information useful for dairy farming. In order to study such combination of several sensors, this PhD will be based on data mining methods, especially pattern mining algorithms. The challenge is to design new algorithms taking into account such data heterogeneity, both from their nature and the different time scales involved, and to produce patterns that are actually useful for dairy farming. This thesis will be an original and important contribution to the new challenge of the IoT and will interest domain actors to find new added value to a global data analysis. The PhD will take place in an interdisciplinary setting between computer scientists of Inria and animal scientists of Inra, both located in Rennes.

Kévin Fauvel is working on this subject of thesis since the 1^st october of 2017 for 3 years. He is supervised by Alexandre Termier (Université de Rennes 1, team Irisa/Inria, Lacodam) and Philippe Faverdin in the team Dairy Systems.

Thesis co-funded by #DigitAg.

Contact

Kévin Fauvel : kevin.fauvel [at] inria.fr