Sensitivity analysis on a multi-output neural network model (with R of 0.931, 0.944, 0.953 for C1, C2, C3, respectively) found C1 with the highest water saving ability, that restricted transpiration at relatively low VPD levels, 56% (i.e. Assessment of the distinct p-value groups within each selected feature revealed highest genotypic variation for the feature representing transpiration response to high VPD condition. All the wild relatives were found in C1, while C2 and C3 mostly comprised high TE and low TE lines, respectively. Genotypes were clustered (C1, C2, C3) and 6 most important features (with heritability > 0.5) were selected using unsupervised Random Forest. Therefore, outdoors HTP data (15 min frequency) of a chickpea population were used to automate the generation of smooth transpiration profiles, extract informative features of the transpiration response to VPD for optimal genotypic discretization, identify VPD breakpoints, and compare genotypes.ResultsFifteen biologically relevant features were extracted from the transpiration rate profiles derived from load cells data.

Further, no study has precisely identified the VPD breakpoints where genotypes restrict transpiration under natural conditions. A few high-throughput phenotyping (HTP) studies exist, and have considered only maximum transpiration rate in analyzing genotypic differences in this trait. However, it is often measured under controlled conditions and at very low throughput, unsuitable for breeding. BackgroundRestricting transpiration under high vapor pressure deficit (VPD) is a promising water-saving trait for drought adaptation.

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