Table 7 Performance of various ANN models in forecasting measured parameters and SY of dry bean crop using different SRIs.

From: Water status and plant traits of dry bean assessment using integrated spectral reflectance and RGB image indices with artificial intelligence

Model

Optimal SRIs

Hyper-parameter

(Z, L, N, I)

Training

Testing

R2

RMSE

NRMSE (%)

R2

RMSE

NRMSE (%)

ANNB-WB1

\(\:{\text{S}\text{R}\text{I}}_{\text{580,1130}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{586,1130}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{636,630}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{642,632}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{648,622}}\)

(Tanh, 1, 6, 700)

0.94***

3.09

6.15

0.93***

2.48

4.63

ANNB-DB1

\(\:{\text{S}\text{R}\text{I}}_{\text{574,1134}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{580,1130}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{586,1130}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{636,630}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{642,632}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{648,622}}\), \(\:{\text{S}\text{R}\text{I}}_{662,\:610}\), \(\:{\text{S}\text{R}\text{I}}_{\text{970,946}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{1104,710}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{1120,1142}}\), PRMI,\(\:{\text{N}\text{D}\text{I}}_{\text{570,540}}\), NAI

(Logistic, 2, 4, 600)

0.96***

0.26

3.36

0.88***

0.33

4.08

ANNB -CMC1

\(\:{\text{S}\text{R}\text{I}}_{\text{586,1130}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{636,630}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{642,632}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{648,622}}\), \(\:{\text{S}\text{R}\text{I}}_{662,\:610}\), \(\:{\text{S}\text{R}\text{I}}_{\text{970,946}}\)

(Relu, 1, 8, 500)

0.88***

0.53

0.63

0.86***

0.45

0.53

ANNB-SPAD1

\(\:{\text{S}\text{R}\text{I}}_{\text{574,1134}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{580,1130}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{586,1130}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{636,630}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{642,632}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{648,622}}\), \(\:{\text{S}\text{R}\text{I}}_{662,\:610}\), \(\:{\text{S}\text{R}\text{I}}_{\text{970,946}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{1104,710}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{1120,1142}}\), NWI-1, NWI-3, NWI-4, NWI-5, \(\:{\text{N}\text{D}\text{I}}_{\text{570,540}}\), \(\:{\text{N}\text{D}\text{I}}_{\text{686,620}}\), WI

(Tanh, 1, 5, 500)

0.97***

0.43

1.07

0.97***

0.46

1.12

ANNB-SWC1

\(\:{\text{S}\text{R}\text{I}}_{\text{574,1134}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{580,1130}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{586,1130}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{636,630}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{642,632}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{648,622}}\), \(\:{\text{S}\text{R}\text{I}}_{662,\:610}\), \(\:{\text{S}\text{R}\text{I}}_{\text{1104,710}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{1120,1142}}\),

(Logistic, 1, 8, 600)

0.97***

0.96

5.53

0.95***

1.26

6.99

ANNB-SY1

\(\:{\text{S}\text{R}\text{I}}_{\text{574,1134}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{580,1130}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{586,1130}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{636,630}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{642,632}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{648,622}}\), \(\:{\text{S}\text{R}\text{I}}_{662,\:610}\), \(\:{\text{S}\text{R}\text{I}}_{\text{970,946}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{1104,710}}\), \(\:{\text{S}\text{R}\text{I}}_{\text{1120,1142}}\), PRMI,\(\:{\text{N}\text{D}\text{I}}_{\text{570,540}}\), NAI

(Logistic, 1, 5, 700)

0.98***

1.14

3.02

0.98***

0.99

2.49

  1. Z, L, N, and I represent activation function, layers number, neurons number in each layer, and iterations number, respectively.
Back to article page