Non-invasive phenotyping for water and nitrogen uptake by deep roots explored using machine learning
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Non-invasive phenotyping for water and nitrogen uptake by deep roots explored using machine learning. / Changdar, Satyasaran; Popovic, Olga; Wacker, Tomke Susanne; Markussen, Bo; Dam, Erik Bjørnager; Thorup-Kristensen, Kristian.
In: Plant and Soil, Vol. 493, 2023, p. 603–616.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Non-invasive phenotyping for water and nitrogen uptake by deep roots explored using machine learning
AU - Changdar, Satyasaran
AU - Popovic, Olga
AU - Wacker, Tomke Susanne
AU - Markussen, Bo
AU - Dam, Erik Bjørnager
AU - Thorup-Kristensen, Kristian
N1 - Publisher Copyright: © 2023, The Author(s).
PY - 2023
Y1 - 2023
N2 - Background and aims: Root distribution over the soil profile is important for crop resource uptake. Using machine learning (ML), this study investigated whether measured square root of planar root length density (Sqrt_pRLD) at different soil depths were related to uptake of isotope tracer (15N) and drought stress indicator (13C) in wheat, to reveal root function. Methods: In the RadiMax semi-field root-screening facility 95 winter wheat genotypes were phenotyped for root growth in 2018 and 120 genotypes in 2019. Using the minirhizotron technique, root images were acquired across a depth range from 80 to 250 cm in May, June, and July and RL was extracted using a convolutional neural network. We developed ML models to explore whether the Sqrt_pRLD estimates at different soil depths were predictive of the uptake of deep soil nitrogen - using deep placement of 15N tracer as well as natural abundance of 13C isotope. We analyzed the correlations to tracer levels to both a parametrized root depth estimation and an ML approach. We further analyzed the genotypic effects on root function using mediation analysis. Results: Both parametrized and ML models demonstrated clear correlations between Sqrt_pRLD distribution and resource uptake. Further, both models demonstrated that deep roots at approx. 150 to 170 cm depth were most important for explaining the plant content of 15N and 13C isotopes. The correlations were higher in 2018. Conclusions: The results demonstrated that, parametrized models and ML-based analysis provided complementary insight into the importance of deep rooting for water and nitrogen uptake.
AB - Background and aims: Root distribution over the soil profile is important for crop resource uptake. Using machine learning (ML), this study investigated whether measured square root of planar root length density (Sqrt_pRLD) at different soil depths were related to uptake of isotope tracer (15N) and drought stress indicator (13C) in wheat, to reveal root function. Methods: In the RadiMax semi-field root-screening facility 95 winter wheat genotypes were phenotyped for root growth in 2018 and 120 genotypes in 2019. Using the minirhizotron technique, root images were acquired across a depth range from 80 to 250 cm in May, June, and July and RL was extracted using a convolutional neural network. We developed ML models to explore whether the Sqrt_pRLD estimates at different soil depths were predictive of the uptake of deep soil nitrogen - using deep placement of 15N tracer as well as natural abundance of 13C isotope. We analyzed the correlations to tracer levels to both a parametrized root depth estimation and an ML approach. We further analyzed the genotypic effects on root function using mediation analysis. Results: Both parametrized and ML models demonstrated clear correlations between Sqrt_pRLD distribution and resource uptake. Further, both models demonstrated that deep roots at approx. 150 to 170 cm depth were most important for explaining the plant content of 15N and 13C isotopes. The correlations were higher in 2018. Conclusions: The results demonstrated that, parametrized models and ML-based analysis provided complementary insight into the importance of deep rooting for water and nitrogen uptake.
KW - 13C
KW - 15N
KW - Deep resource uptake
KW - Deep rooting
KW - Machine learning
KW - Random forest
U2 - 10.1007/s11104-023-06253-7
DO - 10.1007/s11104-023-06253-7
M3 - Journal article
AN - SCOPUS:85170068818
VL - 493
SP - 603
EP - 616
JO - Plant and Soil
JF - Plant and Soil
SN - 0032-079X
ER -
ID: 366990627