Prediction of sunflower leaf area at vegetative stage by image analysis and application to the estimation of water stress response parameters in post-registration varieties

Abstract

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.