Section: Application Domains

Agriculture and environment

  • Dairy Farming. The use and analysis of data acquired in dairy farming is a challenge both for data science and animal science. The goal is to improve farming conditions, i.e., health, welfare and environment, as well as farmers’ income. Nowadays, animals are monitored by multiple sensors giving a wealth of heterogeneous data such as temperature, weight, or 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. The PhD thesis of Kevin Fauvel (#DigitAg grant), aims to study such combinations of sensors and to investigate the use data mining methods, especially pattern mining algorithms. The challenge is to design new algorithms that take into account data heterogeneity —in terms of nature and time units—, and that produce useful patterns for dairy farming. The outcome of this thesis will be an original and important contribution to the new challenge of the IoT (Internet of Things) and will interest domain actors to find new added value to a global data analysis. The PhD thesis, started on October 2017, takes place in an interdisciplinary setting bringing together computer scientists from Inria and animal scientists from INRA, both located in Rennes.

    Similar problems are investigated with the veterinary department of the University of Calgary in the context of cattle monitoring from multiple sensors placed on calves for the early detection of diseases.

  • Optimizing the Nutrition of Individual Sow. Another direction for further research is the combination of data flows with prediction models in order to learn nutrition strategies. Raphaël Gauthier started a PhD thesis (#DigitAg Grant) in November 2017 with both Inria and INRA supervisors. His research addresses the problem of finding the optimal diet to be supplied to individual sows. Given all the information available, e.g., time-series information about previous feeding, environmental data, scientists models, the research goal is to design new algorithms to determine the optimal ration for a given sow in a given day. Efficiency issues of developed algorithms will be considered since the proposed software should work in real-time on the automated feeder. The decision support process should involve the stakeholder to ensure a good level of acceptance, confidence and understanding of the final tool.

  • Ecosystem Modeling and Management. Ongoing research on ecosystem management includes modelling of ecosystems and anthroprogenic pressures, with a special concern on the representation of socio-economical factors that impact human decisions. A main research issue is how to represent these factors and how to integrate their impact on the ecosystem simulation model. This work is an ongoing cooperation with ecologists from the Marine Spatial Ecology of Queensland University, Australia and from Agrocampus Ouest.

  • Numerical Rule Mining for Prediction of Wheat and Vine diseases. Wheat and vine crops are crucial for the economy of France. Alas, they both suffer from threatening diseases. The fight against crop diseases is often implemented through the use of myriads of phytosanitary products, which raise concerns in regards to public health and environmental impact. In order to control the use of these products, agronomists have developed statistical models to understand the dynamics of diseases and reduce the utilization of phytosanitary products. The internship of Olivier Pelgrin, financed by #DigitAg and supervised in collaboration with the Acta (http://www.acta.asso.fr/) and the IFV (Institut Français de la Vigne), was concerned with the development of a data mining method capable of extracting hybrid expert rules from observations of vine and wheat diseases. Hybrid rules combine patterns such as 𝑣𝑎𝑟𝑖𝑒𝑡𝑦=``𝐺𝑟𝑒𝑛𝑎𝑐ℎ𝑒" with regression models, e.g., 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒=α×𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒+β. Such rules are conceived to aid the study of wheat and vine diseases. The rules are meant to be interpretable, i.e., as concise as possible, and globally accurate, thus they constitute a pattern-aided regression method that has shown good prediction performance. The resulting method, called HIPAR (Hierarchical Interpretable Pattern-aided regression), is currently under submission at the SIAM Conference on Data Mining (SDM19).