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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1093/jxb/erad129
|2 doi
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|a pubmed24n1225.xml
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|a (DE-627)NLM355231689
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|a (NLM)37018460
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Wijewardane, Nuwan K
|e verfasserin
|4 aut
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| 245 |
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|a A leaf-level spectral library to support high-throughput plant phenotyping
|b predictive accuracy and model transfer
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| 264 |
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Completed 07.08.2023
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|a Date Revised 13.12.2023
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|a published: Print
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|a Citation Status MEDLINE
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|a © The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Experimental Biology.
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|a Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive, and models show poor transferability among different datasets. This study had three specific objectives: first, to assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum; second, to evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur); and third, to investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (mean R2=0.688), with partial least-squares regression outperforming deep neural network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (mean R2=0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (mean R2=0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping, whilst extra-weight spiking improves model transferability and extends its utility
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|a Journal Article
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|a Research Support, U.S. Gov't, Non-P.H.S.
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|a Biochemical traits
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|a camelina
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|a extra-weighted spiking
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| 650 |
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|a high-throughput phenotyping
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| 650 |
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|a leaf hyperspectral reflectance
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| 650 |
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|a machine-learning
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| 650 |
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|a maize
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| 650 |
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|a partial least squares regression
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| 650 |
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|a sorghum
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| 650 |
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4 |
|a soybean
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| 650 |
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|a trait modeling
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| 650 |
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|a Chlorophyll
|2 NLM
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| 650 |
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|a 1406-65-1
|2 NLM
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| 700 |
1 |
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|a Zhang, Huichun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Jinliang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Schnable, James C
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Schachtman, Daniel P
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ge, Yufeng
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Journal of experimental botany
|d 1985
|g 74(2023), 14 vom: 03. Aug., Seite 4050-4062
|w (DE-627)NLM098182706
|x 1460-2431
|7 nnns
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| 773 |
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|g volume:74
|g year:2023
|g number:14
|g day:03
|g month:08
|g pages:4050-4062
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| 856 |
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|u http://dx.doi.org/10.1093/jxb/erad129
|3 Volltext
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|d 74
|j 2023
|e 14
|b 03
|c 08
|h 4050-4062
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