Designing new data mining methods, appropriate to agricultural data, to extract actionable knowledge
Objectives:
To design new data mining methods, appropriate for the big data characteristics of agriculture and preserving privacy
To develop visual and interactive methods for data analysis, tailored for non-specialists
The real value of the growing volume of agricultural data (see Axis 3, 4) lies in its potential to be converted into actionable knowledge to provide agriculture with high-added value advisory services. Currently, no application efficiently exploits all the richness hidden in such data, that possess specific characteristics (time/ spatial dimension). There is a need for an integrated approach, combining data from monitoring systems in farms (crops, animals, buildings), from farmers, from scientists and technicians about technical itineraries as well as crowd-testing made by consumers.
Agricultural data are,at the same time, continuously growing in volume, while at the same time possessing multi-scale (time, space), uncertain, dynamic, and heterogenous features. They can also be sensitive in nature and may require solutions that preserve privacy. Unfortunately, there is no data mining approach capable of simultaneously handling all the features characterizing agricultural data. The work hypothesis is to build a methodology based on the broad spectrum of expertise and skills in statistical data analysis, data mining and integration of imperfect knowledge that the #DigitAg teams have developed for other sectors (e.g. medical sector). The proposed methods will be co-designed with domain actors (data scientists, advisor experts, farmers) and interaction with actors will take place at different levels, either to receive domain knowledge, or help specify the problem at hand or to present results. An important challenge will be the definition of interactive methods specifically tailored for non-specialists (ex: visual analytics, justification and explanation). These methods will lead to a major breakthrough in the practical exploitation of agricultural Big Data. A precise framework for the evaluation of results will be set up along the project duration, based on statistical model validation, the agronomist expertise (INRA, ACTA) and collaboration with other domain actors. Such a framework represents a resolutely innovative aspect in the data-mining and statistical analysis domain. Assessing current results on agricultural time scales (seasons, years) will allow us to continuously improve the proposed methods.
Leaders: Alexandre Termier (INRIA, on the right in the pic), Pascal Poncelet (University of Montpellier)
Units: LACODAM (Rennes), ZENITH & GRAPHIK , LIRMM, IATE, MISTEA , PEGASE
Disciplines:  Primary Data sciences, Computing sciences; Secondary Agronomy (knowledge on data quality issues; knowledge extraction utputs); Social sciences (human-centered design of processing/visualisation methods).
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