Table 7 The performance and complexity comparison between the proposed CACU model vs state-of-the-art approaches.

From: Enhancing the weed segmentation in diverse crop fields using computationally effective concatenated attention U-Net with convolutional block attention module

Crop

Model/approach name

A

MIoU

P

R

NPM

%

%

%

%

Million

Carrot

DeepLabV3++28

84.3

11.85

Adapted-IV39

93.9

25

AgNet9

88.9

 

0.25

RRUDC10

95.40

95.43

98.82

0.655

Proposed CAUC

98.38

80.5

97.51

97.27

0.377

Sugar Beet

Deep encoder-decoder CNN(Bonn)53

94.74

80.1

UNet-ResNet50(dice + focal)29

96.06

85.25

92.28

92.21

20.67

Bonnet26

99.32

77.47

1.1

UNet-ResNet5021

67.0

20.67

Proposed CAUC

98.51

80.75

97.54

97.50

0.377

Sun flower

VGG-UNet31

90.0

64.0

Bonnet21

70.0

  

1.1

UNet-ResNet5021

43.0

  

20.67

Bonnet26

99.02

68.98

1.1

Proposed CAUC

99.10

81.1

99.14

99.10

0.377

  1. A - Accuracy, P – Precision, R - Recall, S – Model Size, NPM - Number of Parameters generated by the Model, - Not given in the paper.