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Research on Crop Detection Algorithm Based on Improved YOLOv7

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16 juin 2025
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Figure 1.

YOLOv7 network architecture diagram
YOLOv7 network architecture diagram

Figure 2.

Schematic diagram of Multi_Concat_Block module
Schematic diagram of Multi_Concat_Block module

Figure 3.

Schematic diagram of Transition Block module
Schematic diagram of Transition Block module

Figure 4.

Sketch of SPPCSPC structure
Sketch of SPPCSPC structure

Figure 5.

Feature layer shape change map
Feature layer shape change map

Figure 6.

Map of the location of the introduction of the attention mechanism
Map of the location of the introduction of the attention mechanism

Figure 7.

Roadmap for system realization
Roadmap for system realization

Figure 8.

Confusion matrix diagram
Confusion matrix diagram

Figure 9.

F1 score graph
F1 score graph

Figure 10.

P_curve
P_curve

Figure 11.

PR_curve
PR_curve

Figure 12.

R_curve
R_curve

Figure 13.

Apple experiment result
Apple experiment result

Figure 14.

Results of Onion and Carrot experiments
Results of Onion and Carrot experiments

Table of fruit types and corresponding number of pictures

Name and number of vegetables Name and number of fruit
Cabbage (200) Apple (200)
Capsicum (200) Banana (200)
Carrot (200) Pear (200)
Cauliflower (200) Pineapple (200)
Corn (200) Pomegranate (200)
Eggplant (200) Grapes (200)
Cabbage (200) Apple (200)

Comparison table of detection accuracy

Type Evaluation metrics
Detection Times mAP/% (Pre-improved) mAP/% (Improved)
Apple 30 0.79 0.85
Banana 30 0.74 0.77
Pear 30 0.79 0.81
Pineapple 30 0.81 0.79
Pomegranate 30 0.68 0.68
Grapes 30 0.50 0.57
Watermelon 30 0.73 0.78
Cabbage 30 0.89 0.91
Capsicum 30 0.55 0.58
Carrot 30 0.83 0.89
Cauliflower 30 0.55 0.64
Corn 30 0.46 0.45
Eggplant 30 0.74 0.71
Onion 30 0.88 0.95
Langue:
Anglais
Périodicité:
4 fois par an
Sujets de la revue:
Informatique, Informatique, autres