  The automatic measurement of developmental and physiological responses of
sunflowers to water stress represents an applied challenge for a better
knowledge of the varieties available to growers, but also a fundamental one for
identifying the biological, genetic and molecular bases of plant response to
their environment.On INRAE Toulouse's Heliaphen high-throughput phenotyping
platform, we set up two experiments, each with 8 varieties (2*96 plants), and
acquired images of plants subjected or not to water stress, using a light
barrier on a daily basis. At the same time, we manually measured the leaf
surfaces of these plants every other day for the duration of the stress, which
lasted around ten days. The images were analyzed to extract morphological
characteristics of the segmented plants and different models were evaluated to
estimate total plant leaf areas using these data.A linear model with a
posteriori smoothing was used to estimate total leaf area with a relative
squared error of 11% and an efficiency of 93%. Leaf areas estimated
conventionally or with the developed model were used to calculate the leaf
expansion and transpiration responses (LER and TR) used in the SUNFLO crop
model for 8 sunflower varieties studied. Correlation coefficients of 0.61 and
0.81 for LER and TR respectively validate the use of image-based leaf area
estimation. However, the estimated values for LER are lower than for the manual
method on Heliaphen, but closer overall to the manual method on
greenhouse-grown plants, potentially suggesting an overestimation of stress
sensitivity.It can be concluded that the LE and TR parameter estimates can be
used for simulations. The low cost of this method (compared with manual
measurements), the possibility of parallelizing and repeating measurements on
the Heliaphen platform, and of benefiting from the Heliaphen platform's data
management, are major improvements for valorizing the SUNFLO model and
characterizing the drought sensitivity of cultivated varieties.
