NirsPredict is an application allowing to make predictions about phenotypic traits of Arabidopsis thaliana by submiting related NIRS spectra using deep-learning. NirsPredict has three functionalities :
Submitted files must have headers
A dataset with at least 100 spectra is advised to get high-quality predictions
Download and consult the application manual above to get more details on input format and functionnalitiesThe prediction's robustness and the number of input to provide will rely on the selected mode.
Select some traits or/and metabolites to predict. Beware that the more you select the longer it will be.
Visualize and download data from the database built from Vasseur et al. (2022)
Choose query output format
Customize the features present in the provided dataset
We have used the application to predict values for every traits and from every spectra contained in the database.
All known values have been replaced by predicted values to make a complete predicted dataset.
The submitted dataset will be examined and if relevant it may be integrated to the database to extend it and make future predictions more precise
NirsPredict is a tool allowing to make deep-learning predictions about phenotypic traits of A. thaliana by submiting related NIRS spectra
'A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy' - Vasseur et al.
You can contact us on this email adress : NirsPredict@post.com
How to cite: Comming soon