Identifying developmental patterns in plant phenotyping data

Guédon Yann. 2018. Identifying developmental patterns in plant phenotyping data. . European Mathematical Society (EMS) and ESMTB. Lisbonne : European Mathematical Society (EMS)-ESMTB, 1 p. European Conference on Mathematical and Theoretical Biology. Minisymposium Plant Models (A): Plant Development.. 11, Lisbonne, Portugal, 23 Juillet 2018/27 Juillet 2018.

Url - éditeur : http://www.ecmtb2018.org/files/files/Monday/MS_Monday/MS_Ptashnyk.pdf

Note générale : Yann Guédon est l'expert invité du Cirad.

Résumé : The emergence of robotized plant phenotyping platforms and new generations of sensors makes available to biologists a huge amount of spatio-temporal plant data of high quality from the tissular to the whole plant scale. A strong e_ort has been put on sensor output treatment and high-throughput data management. Comparatively, the identi_cation and characterization of complex plant developmental patterns using state-of-the-art methods at the crossroad between probabilistic models, statistical inference, machine learning and pattern recognition has been neglected. Hence, only a small proportion of the information contained in plant phenotyping data is really exploited. The objective of this presentation will be to show how to _ll this gap transposing the approaches that made the success of computational molecular biology and quantitative ecology in the past decades. The identi_cation of developmental patterns in plant phenotyping data will be illustrated on selected examples concerning both the root system and the above ground part of plants and both the tissular and the macroscopic scales.

Auteurs et affiliations

• Guédon Yann, CIRAD-BIOS-UMR AGAP (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/589433/)

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