Assessing nitrogen variability at early stages of maize using mobile fluorescence sensing
##-----------------------------------------------------------------------------------------------------------------------------------------------------------
Copyright (C) 2022 by PrecisionAg Lab, Agronomy, KSU
##-----------------------------------------------------------------------------------------------------------------------------------------------------------
(1) Signal denoising and outlier removal
Folder: Signal_denoising_outlier
Data description:
ardec0710_excel.xlsx contains sample fluorescenec data (Multiplex3) over several plots. These are raw signal.
Fluo_waveletTransform_signalDenoise.py performs wavelet transform based signal denoising for each plot individually.
Data_cleaning_IQR.py performs data ommision (based on FRF_R threshold) and outlier removal using IQR method.
(2) Combile_all_plots>>Combile_data_plots.py Combining fluorescence based vegetation indices over all plots together into a single file.
(3) SVR regression model training and test SVR_Regression>>SVR_Biomass_V9.py Sample data: Ardec2012_Regression_Data (Sheet1#trainV9 and Sheet2#testV9)