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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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Plant Methods

ISSN: 1746-4811