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Table 2 Results of simple nonlinear regression with tenfold cross-validation

From: Soybean leaf estimation based on RGB images and machine learning methods

Dependent varfable

Independent variable

Model type

Model paramenters

MAE

R2

ATPA (%)

a

b

c

LN

SCA1

A

7.23 ± 0.38

0.10 ± 0.01

−5.05 ± 0.73

30.12

0.77

49.92

SCA1

B

0.36 ± 0.01

0.82 ± 0.21

–

30.05

0.77

55.14

SPA1

A

6.92 ± 0.39

0.09 ± 0.01

−4.95 ± 0.76

30.01

0.77

51.02

SPA1

B

0.35 ± 0.01

0.82 ± 0.12

–

30.04

0.77

55.01

SSC

A

−11.38 ± 0.28

4.45 ± 0.08

0.03 ± 0.01

28.63

0.79

47.46

SSC

B

1.83 ± 0.12

1.29 ± 0.41

–

28.45

0.79

50.94

LFW

SCA1

A

−2.43 ± 0.30

0.05 ± 0.02

1.79 ± 0.31

9.17

0.88

43.21

SCA1

B

0.03 ± 0.00

1.07 ± 0.22

–

9.93

0.88

55.95

SPA1

A

−2.56 ± 0.30

0.05 ± 0.01

2.27 ± 0.39

10.23

0.88

43.57

SPA1

B

0.03 ± 0.01

1.07 ± 0.03

–

9.93

0.88

55.57

SSC

A

−4.50 ± 0.30

1.11 ± 0.05

0.04 ± 0.01

9.38

0.89

48.99

SSC

B

0.23 ± 0.04

1.67 ± 0.12

–

6.25

0.89

51.34

LAI

SCA1

A

−405.78 ± 1.40

10.46 ± 0.01

0.01 ± 0.00

2242.61

0.91

59.90

SCA1

B

4.57 ± 0.32

1.12 ± 0.04

–

2158.91

0.91

73.99

SPA1

A

−433.34 ± 1.60

10.38 ± 0.01

0.01 ± 0.00

2257.25

0.91

60.15

SPA1

B

4.38 ± 0.26

1.12 ± 0.15

–

2161.84

0.91

73.95

SSC

A

−556.15 ± 17.93

184.22 ± 2.23

13.10 ± 0.01

2063.22

0.91

57.03

SSC

B

43.68 ± 5.43

1.75 ± 0.13

–

2031.49

0.92

64.31

  1. RMSE, R2, and ATPA represent the average prediction accuracy; Model A and Model B represent the polynomial quadratic function (y = a + bx + cx2) and exponential function (y = axb), respectively