The Plant Phenome JournalBioinformaticsPeer-reviewed journal article
Making WAVES in Breedbase: An Integrated Spectral Data Storage and Analysis Pipeline for Plant Breeding Programs
Jenna L. HershbergeriD, Nicolás MoralesiD, Christiano C. SimoesiD, Bryan J. EllerbrockiD, Guillaume Jean BauchetiD, Lukas A. MuelleriD, Michael A. GoreiD
Abstract
Visible and near-infrared (vis-NIRS) spectroscopy is a promising tool for increasing phenotyping throughput in plant breeding programs, but existing analysis software packages are not optimized for a breeding context. Additionally, commercial software options are often outside of budget constraints for some breeding and research programs. To that end, we developed an open-source R package, waves, for the streamlined analysis of spectral data with several cross-validation schemes to assess prediction accuracy. Waves is compatible with a wide range of spectrometer models and performs visualization, filtering, aggregation, cross-validation set formation, model training, and prediction functions for the association of vis-NIRS spectra with reference measurements. Furthermore, we have integrated this package into the Breedbase family of open-source databases, expanding the analysis capabilities of this growing digital ecosystem to a number of crop species. Taken together, the standalone and Breedbase versions of waves enhance the accessibility of tools for the analysis of spectral data during the plant breeding process. Core ideasO_LIwaves is an open-source R package for spectral data analysis in plant breeding C_LIO_LIBreeding relevant cross-validation schemes to evaluate predictive accuracy of models C_LIO_LIExtension of Breedbase--an open-source database--to support spectral data storage C_LIO_LIGraphical user interface developed for implementation of waves in Breedbase C_LI
- organism: plant (other)
- assay: imaging
- research focus: benchmarking
- research focus: database / resource
- research focus: software / tool
- approach: statistical model
- Citations
- 25
- FWCI
- 1.38
- Field percentile
- top 25%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Low-cost, handheld near-infrared spectroscopy for root dry matter content prediction in cassava
J. Hershberger, E. G. N. Mbanjo, … M. A. Gore·bioRxiv·Apr 30, 2021
- Factors Influencing Phenomic Prediction: A Case Study on a Large Sorghum BCNAM Population
Clément Bienvenu, Vincent Garin, … Hugues de Verdal·bioRxiv·Feb 4, 2025
- Quantifying physiological trait variation with automated hyperspectral imaging in rice
T.-C. Ting, A. Souza, … D. R. Wang·bioRxiv·Dec 14, 2022
- Near-infrared spectroscopy outperforms genomic selection for predicting sugarcane feedstock quality traits
Mateus Teles Vital Gonçalves, Gota Morota, … Luíz Alexandre Peternelli·PLoS ONEPeer-reviewed·Mar 4, 2021
- OPTIMIZING PRE-PROCESSING OF NEAR INFRARED SPECTRA FOR PHENOMIC PREDICTION USING SINGULAR VALUE DECOMPOSITION
Clément Bienvenu, Jean‐Michel Roger, … Vincent Segura·bioRxiv·Aug 23, 2026
- Optimizing Signal Acquisition and Chemometric Pipelines for Micro NIR Plant Identification: Evaluating Spectral Backgrounds and Data Processing in Herbarium Specimens
Thiago Caíque Alves, André Luís de Gasper·bioRxiv·Jul 7, 2026