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

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.

Plant phenotyping relevance

葉のハイパースペクトルデータライブラリを構築し、機械学習による複数の葉形質推定と外部データへの転移性を評価することが研究の中心であり、表現型取得・推定手法の開発および検証に該当する。

Code and data availability

The supplied blocks describe a leaf-level VIS-NIR-SWIR spectral library (n=2460) and PLSR/DNN modeling, but contain no authors' public deposit, repository, or availability statement for the spectral data, trait measurements, or analysis code. The only URL mentioned (soilspectroscopy.org) refers to an unrelated soil-sci

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