Skip to main content

Table 3 Performance criteria of DTs for peanut/weed classification

From: Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features

DT

Feature selection/reduction method

Train

Test

Kappa

RMSE

Accuracy (%)

Kappa

RMSE

Accuracy (%)

J48

–*

0.88

0.2472

93.89

0.83

0.2791

91.67

CFS

0.91

0.1942

95.56

0.88

0.1968

93.75

PCA

0.63

0.4200

81.67

0.63

0.4210

81.48

REP

–*

0.84

0.2679

92.22

0.82

0.2888

90.91

CFS

0.86

0.2557

92.78

0.83

0.2895

91.67

PCA

0.47

0.4653

73.33

0.48

0.4986

74.074

RT

–*

0.70

0.3873

85.00

0.70

0.3849

85.18

CFS

0.87

0.2582

93.33

0.85

0.2722

92.59

PCA

0.46

0.5217

72.78

0.42

0.5401

70.83

  1. Bolditalic value indicates the most accurate DT classifier
  2. * No feature selection applied (classification using all input features)