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The objectives and methods of phenotyping are therefore revisited in terms of :
    Throughput. The new genetic approaches require the phenotypic analysis of hundreds of genotypes, i.e. thousands of plants.
Progress is needed to acquire phenotypic information at high throughput. This implies progress in imaging (different spectra) and new sensors.
    Approaches and methods. Whole genome approaches have mostly been tested on simple phenotypes such as flowering date or metabolic traits, which do not require sophisticated phenotyping methods. The same approach for tolerance to biotic or abiotic stresses requires consideration of the marker X environment interaction, i.e. the genetic value of a marker depends on the environmental scenario in which the plants grow.
This requires, (i) fine control or at least measurement of the environmental conditions experienced by the plants, (ii) in situ measurement of plant response under contrasting environmental conditions, (iii) heritable indicators of plant metabolism and growth as a function of environmental conditions. Because of the wide range of environmental scenarios and allelic combinations, modelling plays an essential role in analysing marker X environment interactions and in predicting the agronomic values of allelic combinations.
    Data management and handling. Repeated measurements on thousands of plants generate large data sets.
This requires (i) the implementation of databases, consistent in terms of ontologies and architecture, (ii) the development of mathematical and statistical methods for data cleaning, temporal data analysis and association of phenotypic traits with genetic markers (iii) interfacing with databases integrating genetic information, tools allowing genetic analyses of traits involving different scales of plant organisation (organ, whole plant, canopy) and different temporal scales (from one second to one month).

PhenoArch has been designed to allow the conduct of phenotyping experiments under controlled conditions with the objective of identifying heritable traits with physiological value and usefulness in modelling (e.g. maximum growth rate or sensitivity to environmental conditions, or calculated variables such as radiation or water use efficiency).