@article{fdi:010079890,
  title = {{A}utomated discretization of 'transpiration restriction to increasing {VPD}' features from outdoors high-throughput phenotyping data},
  author = {{K}ar, {S}. and {T}anaka, {R}. and {K}orbu, {L}. {B}. and {K}holova, {J}. and {I}wata, {H}. and {D}urbha, {S}. {S}. and {A}dinarayana, {J}. and {V}adez, {V}incent},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground {R}estricting transpiration under high vapor pressure deficit ({VPD}) is a promising water-saving trait for drought adaptation. {H}owever, it is often measured under controlled conditions and at very low throughput, unsuitable for breeding. {A} few high-throughput phenotyping ({HTP}) studies exist, and have considered only maximum transpiration rate in analyzing genotypic differences in this trait. {F}urther, no study has precisely identified the {VPD} breakpoints where genotypes restrict transpiration under natural conditions. {T}herefore, 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. {R}esults {F}ifteen biologically relevant features were extracted from the transpiration rate profiles derived from load cells data. {G}enotypes were clustered ({C}1, {C}2, {C}3) and 6 most important features (with heritability > 0.5) were selected using unsupervised {R}andom {F}orest. {A}ll the wild relatives were found in {C}1, while {C}2 and {C}3 mostly comprised high {TE} and low {TE} lines, respectively. {A}ssessment of the distinct p-value groups within each selected feature revealed highest genotypic variation for the feature representing transpiration response to high {VPD} condition. {S}ensitivity analysis on a multi-output neural network model (with {R} of 0.931, 0.944, 0.953 for {C}1, {C}2, {C}3, respectively) found {C}1 with the highest water saving ability, that restricted transpiration at relatively low {VPD} levels, 56% (i.e. 3.52 k{P}a) or 62% (i.e. 3.90 k{P}a), depending whether the influence of other environmental variables was minimum or maximum. {A}lso, {VPD} appeared to have the most striking influence on the transpiration response independently of other environment variable, whereas light, temperature, and relative humidity alone had little/no effect. {C}onclusion {T}hrough this study, we present a novel approach to identifying genotypes with drought-tolerance potential, which overcomes the challenges in {HTP} of the water-saving trait. {T}he six selected features served as proxy phenotypes for reliable genotypic discretization. {T}he wild chickpeas were found to limit water-loss faster than the water-profligate cultivated ones. {S}uch an analytic approach can be directly used for prescriptive breeding applications, applied to other traits, and help expedite maximized information extraction from {HTP} data.},
  keywords = {{H}igh throughput phenotyping ; {T}ranspiration rate ; {V}apor pressure deficit ; {T}ime series ; {M}achine learning ; {F}eature selection ; {U}nsupervised random-forest ; {G}ini index ; {N}eural network ; {S}ensitivity analysis},
  booktitle = {},
  journal = {{P}lant {M}ethods},
  volume = {16},
  numero = {1},
  pages = {140 [20 p.]},
  year = {2020},
  DOI = {10.1186/s13007-020-00680-8},
  URL = {https://www.documentation.ird.fr/hor/fdi:010079890},
}