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Table 3 Regression results between seedling counts obtained by the proposed method and human field assessment

From: DeepSeedling: deep convolutional network and Kalman filter for plant seedling detection and counting in the field

Detection model

Testing videos

Regression equation

Video quantity

\(R^{2}\)

RMSE

MAE

MRE (%)

\(model_{SAll}\)

All

Y = X

75

0.98

1.6

1.5

7

\(model_{SAll}\)

TAMU2015

Y = 0.96 X

25

0.85

3.3

3.4

11

\(model_{SAll}\)

UGA2018

Y = X

25

0.99

0.2

0.5

6

\(model_{SAll}\)

UGA2015

Y = 1.01 X

25

0.96

1.1

0.8

4

\(model_{U15U18}\)

TAMU2015

Y = 0.95X

25

0.80

3.9

3.4

11

\(model_{U15T15}\)

UGA2018

Y = 1.02X

25

0.68

0.8

0.6

7

\(model_{T15U18}\)

UGA2015

Y = 0.32X + 18.65

25

0.33

4.3

12.1

57

  1. \(R^{2}\) refers to adjusted \(R^{2}\)
  2. RMSE root mean squared error, MAE mean absolute error, MRE mean relative error