Know more

Our use of cookies

Cookies are a set of data stored on a user’s device when the user browses a web site. The data is in a file containing an ID number, the name of the server which deposited it and, in some cases, an expiry date. We use cookies to record information about your visit, language of preference, and other parameters on the site in order to optimise your next visit and make the site even more useful to you.

To improve your experience, we use cookies to store certain browsing information and provide secure navigation, and to collect statistics with a view to improve the site’s features. For a complete list of the cookies we use, download “Ghostery”, a free plug-in for browsers which can detect, and, in some cases, block cookies.

Ghostery is available here for free:

You can also visit the CNIL web site for instructions on how to configure your browser to manage cookie storage on your device.

In the case of third-party advertising cookies, you can also visit the following site:, offered by digital advertising professionals within the European Digital Advertising Alliance (EDAA). From the site, you can deny or accept the cookies used by advertising professionals who are members.

It is also possible to block certain third-party cookies directly via publishers:

Cookie type

Means of blocking

Analytical and performance cookies

Google Analytics

Targeted advertising cookies


The following types of cookies may be used on our websites:

Mandatory cookies

Functional cookies

Social media and advertising cookies

These cookies are needed to ensure the proper functioning of the site and cannot be disabled. They help ensure a secure connection and the basic availability of our website.

These cookies allow us to analyse site use in order to measure and optimise performance. They allow us to store your sign-in information and display the different components of our website in a more coherent way.

These cookies are used by advertising agencies such as Google and by social media sites such as LinkedIn and Facebook. Among other things, they allow pages to be shared on social media, the posting of comments, and the publication (on our site or elsewhere) of ads that reflect your centres of interest.

Our EZPublish content management system (CMS) uses CAS and PHP session cookies and the New Relic cookie for monitoring purposes (IP, response times).

These cookies are deleted at the end of the browsing session (when you log off or close your browser window)

Our EZPublish content management system (CMS) uses the XiTi cookie to measure traffic. Our service provider is AT Internet. This company stores data (IPs, date and time of access, length of the visit and pages viewed) for six months.

Our EZPublish content management system (CMS) does not use this type of cookie.

For more information about the cookies we use, contact INRA’s Data Protection Officer by email at or by post at:

24, chemin de Borde Rouge –Auzeville – CS52627
31326 Castanet Tolosan CEDEX - France

Dernière mise à jour : Mai 2018

Menu Logo Principal



Leader : Nadine Hilgert (UMR MISTEA)

Contributors: MISTEA – I3M - VIRTUAL PLANT – all the partners associated to the facilities

We develop a set of methods based on computer science, mathematical modelling and statistics, to analyse the large phenotypic datasets in order to extract reproducible traits associated with each genotype.

  • Cleaning datasets for outlier points or plants is inevitable for generating high-quality datasets due to problems of sensors or devices, or wrong identification of plants or seeds. It is relatively easy, based on intuition, for small datasets but require artificial intelligence for thousands of plants and traits for, e.g. 90 days. We developed a software agent that cleans and validates large datasets by automatically mimicking the biologist's reasoning, allowing one to detect potential outliers that may or may not be eventually used in analyses. The detection of 'outlier plants or microplots', i.e. biological replicates deviating from the distribution of plants on a multi-criteria basis, requires elaborate methods combining prior knowledge and statistical tools. We have shown that the absence of cleaning may lead either to the detection of a large number of artefact genomic regions associated with traits of interest, or to the loss of interesting alleles if high threshold are used to avoid such artefacts.
MCP3 outlier

An example of identification of potentially outlier datapoints, which a user may or may not validate based on intuition and, may not use in further analyses

  • Combining phenomic datasets with models of plants or crops, and with statistical models, is an essential challenge for the use of phenomic datasets in the design of new varieties coping with climate change. Indeed, even ambitious phenotyping projects finally involve a limited number of experimental fields (usually 5-30 as a maximum) whereas a much higher number would be necessary for testing genotypes in the real world involving many combination of environmental conditions. Furthermore, many combinations of alleles need to be tested. It is now possible to envisage testing hundreds of genotypes in hundreds of fields in current or future climates. This requires combining genotype-specific traits obtained by phenomics, environmental characterization based on sensor networks or environmental grids, genomic prediction of traits and crop modelling (see highlights 6 and 7).


  • Developing tools for combining phenomics, genomic prediction, environmental characterization and modelling will be a priority for next years in the frame of our "big data" strategy.