Skip to main content

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
Back to article page

Plant Methods

ISSN: 1746-4811