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Table 2 Results for the transferability evaluation of disease detection models

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

     VNIR    SWIR  
    2016/17 2016/18 2017/18 2016/17 2016/18 2017/18
Modeling Original data CA (%) 68 ± 1 72 ± 1 72 ± 1 72 ± 1 73 ± 1 79 ± 1
TPR (%) 71 ± 4 73 ± 3 72 ± 4 79 ± 8 67 ± 5 79 ± 4
FPR (%) 29 ± 4 32 ± 3 22 ± 4 36 ± 7 25 ± 4 25 ± 5
Annotated data CA (%) 86 ± 2 89 ± 1 89 ± 0 86 ± 1 86 ± 3 93 ± 0
TPR (%) 86 ± 3 89 ± 3 90 ± 1 83 ± 3 82 ± 4 90 ± 2
FPR (%) 18 ± 4 12 ± 1 10 ± 1 14 ± 3 13 ± 2 6 ± 2
Application on third year Original data CA (%) 63 62 57 57 62 52
TPR (%) 61 61 47 38 71 51
FPR (%) 19 43 68 35 30 72
Annotated data CA (%) 82 73 59 36 56 49
TPR (%) 85 79 92 47 42 99
FPR (%) 14 16 40 20 31 72
  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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