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Table 2 Performance criteria of DTs for plant type identification

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

–a

0.70

0.3129

77.50

0.64

0.3483

73.61

CFS

0.74

0.2855

80.83

0.73

0.2947

80.56

PCA

0.39

0.4594

54.17

0.36

0.4859

52.77

REP

–a

0.70

0.3135

77.50

0.60

0.3334

75.00

CFS

0.73

0.3007

80.00

0.72

0.3143

79.17

PCA

0.38

0.4121

53.33

0.34

0.4930

51.38

RT

–a

0.62

0.3764

71.67

0.57

0.3953

68.75

CFS

0.72

0.3227

79.17

0.70

0.3371

77.27

PCA

0.40

0.4743

55.00

0.38

0.4556

54.17

  1. Bolditalic value indicates the most accurate DT classifier
  2. aNo feature selection applied (classification using all input features)