ArticleJournal of experimental botany2023
A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer.
Article in Journal of experimental botany, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- VIS-NIR-SWIR Proximal Spectroradiometry Coupled with Machine Learning and Deep Learning for Ornamental Plant Identification and Classification.ACS omega · 2026Article
- Machine learning surrogate for the leaf PROSPECT-D model and its applications across plant species.Scientific reports · 2026Article
- DeepSpecN: A new hybrid method combining PROSPECT-PRO and Conv-Transformer to estimate leaf nitrogen content from leaf reflectance.Plant phenomics (Washington, D.C.) · 2025Article
- Targeted integrating hyperspectral and metabolomic data with spectral indices and metabolite content models for efficient salt-tolerant phenotype discrimination inPlant phenomics (Washington, D.C.) · 2025Article
- Nitrogen sensing and regulatory networks: it's about time and space.The Plant cell · 2024Review
- Advances in the tea plants phenotyping using hyperspectral imaging technology.Frontiers in plant science · 2024Review
- Miniaturized Vis-NIR handheld spectrometer for non-invasive pigment quantification in agritech applications.Scientific reports · 2023Article
- Effects of fine grinding on mid-infrared spectroscopic analysis of plant leaf nutrient content.Scientific reports · 2023Article
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6 authors.
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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.
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