A Leaf-Level Spectral Library to Support High-Throughput Plant Phenotyping: Predictive Accuracy and Model Transfer
ORCID
Wijewardane: https://orcid.org/0000-0001-8962-9451; Yang: https://orcid.org/0000-0002-0999-3518; Schnable: https://orcid.org/0000-0001-6739-5527; Schachtman: https://orcid.org/0000-0003-1807-4369; Ge: https://orcid.org/0000-0002-6460-0780
MSU Affiliation
College of Agriculture and Life Sciences; Department of Agricultural and Biological Engineering; James Worth Bagley College of Engineering
Creation Date
2026-06-30
Abstract
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.
Publication Date
8-3-2023
Publication Title
Journal of Experimental Botany
Publisher
Oxford University Press; Society for Experimental Biology
First Page
4050
Last Page
4062
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Rights
© The Author(s) 2023
Recommended Citation
Wijewardane, N. K., Zhang, H., Yang, J., Schnable, J. C., Schachtman, D. P., & Ge, Y. (2023). A leaf-level spectral library to support high-throughput plant phenotyping: Predictive accuracy and model transfer. Journal of Experimental Botany, 74(14), 4050–4062. https://doi.org/10.1093/jxb/erad129
