NIRSpredict: a platform for predicting plant traits from near infra-red spectroscopy
Summary Near-infrared spectroscopy (NIRS) has become a popular tool for investigating phenotypic variability in plants. We developed the Shiny NIRSpredict application to get predictions of 81 Arabidopsis thaliana phenotypic traits, including classical functional traits as well as a large variety of...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-11-01
|
| Series: | BMC Plant Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12870-024-05776-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846158679637753856 |
|---|---|
| author | Axel Vaillant Grégory Beurier Denis Cornet Lauriane Rouan Denis Vile Cyrille Violle François Vasseur |
| author_facet | Axel Vaillant Grégory Beurier Denis Cornet Lauriane Rouan Denis Vile Cyrille Violle François Vasseur |
| author_sort | Axel Vaillant |
| collection | DOAJ |
| description | Summary Near-infrared spectroscopy (NIRS) has become a popular tool for investigating phenotypic variability in plants. We developed the Shiny NIRSpredict application to get predictions of 81 Arabidopsis thaliana phenotypic traits, including classical functional traits as well as a large variety of commonly measured chemical compounds, based from near-infrared spectroscopy values based on deep learning. It is freely accessible at the following URL: https://shiny.cefe.cnrs.fr/NirsPredict/ . NIRSpredict has three main functionalities. First, it allows users to submit their spectrum values to get the predictions of plant traits from models built with the hosted A. thaliana database. Second, users have access to the database of traits used for model calibration. Data can be filtered and extracted on user’s choice and visualized in a global context. Third, a user can submit his own dataset to extend the database and get part of the application development. NIRSpredict provides an easy-to-use and efficient method for trait prediction and an access to a large dataset of A. thaliana trait values. In addition to covering many of functional traits it also allows to predict a large variety of commonly measured chemical compounds. As a reliable way of characterizing plant populations across geographical ranges, NIRSpredict can facilitate the adoption of phenomics in functional and evolutionary ecology. |
| format | Article |
| id | doaj-art-955b713f26054483b4e0c3a0419ecb79 |
| institution | Kabale University |
| issn | 1471-2229 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Plant Biology |
| spelling | doaj-art-955b713f26054483b4e0c3a0419ecb792024-11-24T12:17:36ZengBMCBMC Plant Biology1471-22292024-11-0124111210.1186/s12870-024-05776-0NIRSpredict: a platform for predicting plant traits from near infra-red spectroscopyAxel Vaillant0Grégory Beurier1Denis Cornet2Lauriane Rouan3Denis Vile4Cyrille Violle5François Vasseur6CEFE, Univ Montpellier, CNRS, EPHE, IRDUMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut AgroUMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut AgroUMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut AgroLEPSE, Univ Montpellier, INRAE, Institut AgroCEFE, Univ Montpellier, CNRS, EPHE, IRDCEFE, Univ Montpellier, CNRS, EPHE, IRDSummary Near-infrared spectroscopy (NIRS) has become a popular tool for investigating phenotypic variability in plants. We developed the Shiny NIRSpredict application to get predictions of 81 Arabidopsis thaliana phenotypic traits, including classical functional traits as well as a large variety of commonly measured chemical compounds, based from near-infrared spectroscopy values based on deep learning. It is freely accessible at the following URL: https://shiny.cefe.cnrs.fr/NirsPredict/ . NIRSpredict has three main functionalities. First, it allows users to submit their spectrum values to get the predictions of plant traits from models built with the hosted A. thaliana database. Second, users have access to the database of traits used for model calibration. Data can be filtered and extracted on user’s choice and visualized in a global context. Third, a user can submit his own dataset to extend the database and get part of the application development. NIRSpredict provides an easy-to-use and efficient method for trait prediction and an access to a large dataset of A. thaliana trait values. In addition to covering many of functional traits it also allows to predict a large variety of commonly measured chemical compounds. As a reliable way of characterizing plant populations across geographical ranges, NIRSpredict can facilitate the adoption of phenomics in functional and evolutionary ecology.https://doi.org/10.1186/s12870-024-05776-0Arabidopsis thalianaFunctional traitsGenetic variabilityMachine learningPhenomicsSecondary metabolites |
| spellingShingle | Axel Vaillant Grégory Beurier Denis Cornet Lauriane Rouan Denis Vile Cyrille Violle François Vasseur NIRSpredict: a platform for predicting plant traits from near infra-red spectroscopy BMC Plant Biology Arabidopsis thaliana Functional traits Genetic variability Machine learning Phenomics Secondary metabolites |
| title | NIRSpredict: a platform for predicting plant traits from near infra-red spectroscopy |
| title_full | NIRSpredict: a platform for predicting plant traits from near infra-red spectroscopy |
| title_fullStr | NIRSpredict: a platform for predicting plant traits from near infra-red spectroscopy |
| title_full_unstemmed | NIRSpredict: a platform for predicting plant traits from near infra-red spectroscopy |
| title_short | NIRSpredict: a platform for predicting plant traits from near infra-red spectroscopy |
| title_sort | nirspredict a platform for predicting plant traits from near infra red spectroscopy |
| topic | Arabidopsis thaliana Functional traits Genetic variability Machine learning Phenomics Secondary metabolites |
| url | https://doi.org/10.1186/s12870-024-05776-0 |
| work_keys_str_mv | AT axelvaillant nirspredictaplatformforpredictingplanttraitsfromnearinfraredspectroscopy AT gregorybeurier nirspredictaplatformforpredictingplanttraitsfromnearinfraredspectroscopy AT deniscornet nirspredictaplatformforpredictingplanttraitsfromnearinfraredspectroscopy AT laurianerouan nirspredictaplatformforpredictingplanttraitsfromnearinfraredspectroscopy AT denisvile nirspredictaplatformforpredictingplanttraitsfromnearinfraredspectroscopy AT cyrilleviolle nirspredictaplatformforpredictingplanttraitsfromnearinfraredspectroscopy AT francoisvasseur nirspredictaplatformforpredictingplanttraitsfromnearinfraredspectroscopy |