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Research on Crop Detection Algorithm Based on Improved YOLOv7 Cover
By: Xiaoqi Shi and  Xin Ye  
Open Access
|Jun 2025

Figures & Tables

Figure 1.

YOLOv7 network architecture diagram

Figure 2.

Schematic diagram of Multi_Concat_Block module

Figure 3.

Schematic diagram of Transition Block module

Figure 4.

Sketch of SPPCSPC structure

Figure 5.

Feature layer shape change map

Figure 6.

Map of the location of the introduction of the attention mechanism

Figure 7.

Roadmap for system realization

Figure 8.

Confusion matrix diagram

Figure 9.

F1 score graph

Figure 10.

P_curve

Figure 11.

PR_curve

Figure 12.

R_curve

Figure 13.

Apple experiment result

Figure 14.

Results of Onion and Carrot experiments

Table of fruit types and corresponding number of pictures

Name and number of vegetablesName 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

TypeEvaluation metrics
Detection TimesmAP/% (Pre-improved)mAP/% (Improved)
Apple300.790.85
Banana300.740.77
Pear300.790.81
Pineapple300.810.79
Pomegranate300.680.68
Grapes300.500.57
Watermelon300.730.78
Cabbage300.890.91
Capsicum300.550.58
Carrot300.830.89
Cauliflower300.550.64
Corn300.460.45
Eggplant300.740.71
Onion300.880.95
Language: English
Page range: 10 - 19
Published on: Jun 16, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Xiaoqi Shi, Xin Ye, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.