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Table 1 HTPP features selected through recursive feature elimination to predict YR Features marked in bold are unique to the timepoint

From: Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning

Model trained on data collected on 2020-07-02 Model trained on data collected on 2020-07-09
Model feature Average rank across all resamples Standard deviation Model feature Average rank across all resamples Standard deviation
PSRI 3.10 2.62 RGI 1.40 0.81
PRI2 3.27 2.35 GI 3.40 3.17
SIPI3 4.63 3.36 PSRI 4.87 2.76
PRI 5.43 4.12 NRI 5.87 4.12
REP_LE 5.53 3.34 Boochs2 6.87 2.49
RGI 5.77 1.89 DPI 7.00 4.17
BRI 10.77 4.44 REP_LE 8.63 4.12
SR9 14.00 6.72 SR10 10.30 4.23
Vogelmann3 17.10 6.82 PRI 12.30 4.69
PRI norm 17.53 9.55 PRI2 13.10 5.36
LRDSI2 18.87 10.83 Datt5 13.27 10.05
PRI (YR Zheng) 20.97 10.97 DWSI4 14.60 8.32
DD 22.07 16.16 CI 15.03 8.93
DPI 22.27 9.50 Vogelmann3 15.40 5.20
ARI 22.57 5.85 SPVI 17.07 8.66
mSR705 22.93 12.24 ARI2 19.43 6.52
SIPI2 25.63 14.27 PRI (YR Zheng) 20.07 6.76
mND705 25.67 14.20 Boochs 20.57 8.70
MTCI 25.77 15.49 NDVI3 21.43 9.62
SR7 26.77 15.03 PRI_norm 22.30 5.07
D1 27.23 12.15 NPQI 22.77 9.13
mSR 27.77 19.16 SR5 25.13 9.02
NIR Camera BNDVI 29.20 11.86 Carter5 28.33 10.49
mNDVI 31.27 15.72 RGB_Biomass 29.23 9.48
MSAVI 31.63 19.99 DD 30.43 16.17
SR8 32.23 13.74 EVI 32.97 10.13
SIPI 32.27 16.97 MCARI2 33.43 13.59
PhRI 32.30 12.07 NPCI 35.50 12.56
BGI 32.73 12.66 DDn 35.67 10.10
NDVI3 32.97 17.46 SRPI 35.67 11.28
OSAVI 33.13 15.26 ClAInt 36.07 13.96
ARI2 33.67 11.50 Sum_Dr2 36.17 15.92
DWSI4 33.80 17.01 Gitelson 36.63 11.03
mREIP 34.27 10.68 SIPI2 37.17 15.58
CI 36.47 12.66 mREIP 38.23 8.80
REP_Li 36.67 11.06 Datt3 38.47 13.72
MCARI2_OSAVI2 37.67 19.27 SR8 38.57 8.62
SAVI 38.07 15.66 mSR 38.60 16.38
Carter 38.17 13.72 SR9 38.87 11.15
SR10 38.47 13.58 MCARI2_OSAVI2 39.07 12.98
Gitelson 38.70 14.70    
NPCI 39.60 14.79    
  1. Variable names highlighted in bold letters denote predictors unqiuely selected through feature elimination at that timepoint