A leaf-level spectral library to support high-throughput plant phenotyping: Predictive accuracy and model transfer

Nuwan K. Wijewardane, Huichun Zhang, Jinliang Yang, James C. Schnable, Daniel P. Schachtman, Yufeng Ge

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)4050-4062
Number of pages13
JournalJournal of experimental botany
Issue number14
StatePublished - Aug 3 2023


  • Biochemical traits
  • camelina
  • extra-weighted spiking
  • high-throughput phenotyping
  • leaf hyperspectral reflectance
  • machine-learning
  • maize
  • partial least squares regression
  • sorghum
  • soybean
  • trait modeling

ASJC Scopus subject areas

  • Physiology
  • Plant Science


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