PYM: a new, affordable, image-based method using a Raspberry Pi to phenotype plant leaf area in a wide diversity of environments.

Background: Plant science uses increasing amounts of phenotypic data to unravel the complex interactions between biological systems and their variable environments. Originally, phenotyping approaches were limited by manual, often destructive operations, causing large errors. Plant imaging emerged as a viable alternative allowing non-invasive and automated data acquisition.

Genetic variation in a grapevine progeny (Vitis vinifera L. cvs Grenache×Syrah) reveals inconsistencies between maintenance of daytime leaf water potential and response of transpiration rate under drought.

In the face of water stress, plants evolved with different abilities to limit the decrease in leaf water potential, notably in the daytime (ΨM). So-called isohydric species efficiently maintain high ΨM, whereas anisohydric species cannot prevent ΨM from dropping as soil water deficit develops. The genetic and physiological origins of these differences in (an)isohydric behaviours remain to be clarified.

PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit.

The high-throughput phenotypic analysis of Arabidopsis thaliana collections requires methodological progress and automation. Methods to impose stable and reproducible soil water deficits are presented and were used to analyse plant responses to water stress. Several potential complications and methodological difficulties were identified, including the spatial and temporal variability of micrometeorological conditions within a growth chamber, the difference in soil water depletion rates between accessions and the differences in developmental stage of accessions the same time after sowing.