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Table 7 Outcome of the approach of spike counting on the test images of 30 plants

From: SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging

Image no

Ground truth

Predicted using model

TP

FP

FN

Precision

Accuracy

F1 score

1

10

9

9

0

0

1.00

1.00

1.00

2

8

7

7

0

0

1.00

1.00

1.00

3

10

9

8

0

0

1.00

1.00

1.00

4

11

10

10

0

1

1.00

0.91

0.95

5

10

10

10

0

0

1.00

1.00

1.00

6

9

9

8

1

0

0.89

0.89

0.94

7

10

9

9

0

0

1.00

1.00

1.00

8

6

6

6

0

0

1.00

1.00

1.00

9

12

11

10

0

1

1.00

0.91

0.95

10

12

12

11

0

1

1.00

0.92

0.96

11

13

12

10

0

1

1.00

0.91

0.95

12

11

10

9

0

1

1.00

0.90

0.95

13

6

6

6

0

0

1.00

1.00

1.00

14

8

8

8

0

0

1.00

1.00

1.00

15

16

15

13

2

1

0.87

0.81

0.90

16

2

2

2

0

0

1.00

1.00

1.00

17

10

10

10

0

0

1.00

1.00

1.00

18

1

1

1

0

0

1.00

1.00

1.00

19

11

10

10

0

0

1.00

1.00

1.00

20

7

7

7

0

0

1.00

1.00

1.00

21

8

7

7

0

1

1.00

0.88

0.93

22

8

7

7

0

1

1.00

0.88

0.93

23

10

10

10

0

0

1.00

1.00

1.00

24

11

10

10

0

1

1.00

0.91

0.95

25

2

2

2

0

0

1.00

1.00

1.00

26

8

8

7

0

0

1.00

1.00

1.00

27

9

8

7

0

1

1.00

0.88

0.93

28

12

10

10

0

2

1.00

0.83

0.91

29

7

7

7

0

0

1.00

1.00

1.00

30

8

8

7

1

0

0.88

0.88

0.93

Average

     

0.99

0.95

0.97