A leaf-level spectral library to support high-throughput plant phenotyping : predictive accuracy and model transfer
© The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Experimental Biology.
| Veröffentlicht in: | Journal of experimental botany. - 1985. - 74(2023), 14 vom: 03. Aug., Seite 4050-4062 |
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| Weitere Verfasser: | , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2023
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| Zugriff auf das übergeordnete Werk: | Journal of experimental botany |
| Schlagworte: | Journal Article Research Support, U.S. Gov't, Non-P.H.S. Biochemical traits camelina extra-weighted spiking high-throughput phenotyping leaf hyperspectral reflectance machine-learning maize partial least squares regression mehr... |
| Zusammenfassung: | © The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Experimental Biology. 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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| Beschreibung: | Date Completed 07.08.2023 Date Revised 13.12.2023 published: Print Citation Status MEDLINE |
| ISSN: | 1460-2431 |
| DOI: | 10.1093/jxb/erad129 |