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Table 1 Results for the detection of Esca leaf symptoms using original field data and annotated data

From: Evaluating the suitability of hyper- and multispectral imaging to detect foliar symptoms of the grapevine trunk disease Esca in vineyards

     VNIR    SWIR  
    2016 2017 2018 2016 2017 2018
Modeling Original data CA (%) 73 ± 2 70 ± 2 77 ± 2 73 ± 2 81 ± 2 80 ± 2
TPR (%) 71 ± 2 73 ± 2 72 ± 2 70 ± 10 82 ± 5 74 ± 10
FPR (%) 29 ± 2 32 ± 2 22 ± 2 34 ± 9 26 ± 4 20 ± 7
Annotated data CA (%) 92 ± 1 90 ± 1 94 ± 1 88 ± 1 95 ± 1 92 ± 1
TPR (%) 89 ± 1 90 ± 1 93 ± 1 86 ± 4 90 ± 1 100 ± 1
FPR (%) 0 ± 1 11 ± 1 5 ± 1 2 ± 7 6 ± 1 5 ± 1
Application per plant Original data CA (%) 81 73 88 74 84 95
TPR (%) 79 76 86 63 80 86
FPR (%) 19 27 12 23 16 5
Annotated data CA (%) 78 75 91 79 91 90
TPR (%) 58 71 71 60 60 71
FPR (%) 17 25 72 21 8 8
  1. For modeling, all pixels were evaluated not considering spatial scales. Developed models were then applied on plant scale using all leaves for majority voting
  2. CA  classification accuracy, TPR  true-positive rate, FPR  false-positive rate
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