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A new cotton aphid image recognition algorithm and software based on YOLOv8
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Pan MA1, 2, 3, Ziheng YANG4, Hu WAN4, Shun HE4, Yuan HUANG5, Shengyong XU1, 2, 3, *
Journal of Intelligent Agricultural Mechanization | 2023, 4(3) : 42 - 49
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Journal of Intelligent Agricultural Mechanization | 2023, 4(3): 42-49
A new cotton aphid image recognition algorithm and software based on YOLOv8
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Pan MA1, 2, 3, Ziheng YANG4, Hu WAN4, Shun HE4, Yuan HUANG5, Shengyong XU1, 2, 3, *
Affiliations
  • 1.College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
  • 2.Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shenzhen 518000, China
  • 3.Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518000, China
  • 4.College of Plant Science & Technology of Huazhong Agricultural University, Wuhan 430070, China
  • 5.College of Horticulture & Forestry Sciences of Huazhong Agricultural University, Wuhan 430070, China
Published: 2023-08-15 doi: 10.12398/j.issn.2096-7217.2023.03.005
Outline
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In order to solve the problems of high difficulty and low efficiency of manual counting in scientific research of cotton aphid population prediction and control, a cotton aphid image recognition algorithm based on YOLO neural network was proposed and developed into software. First, the images of artificially inoculated cotton aphids were taken by mobile phone for 15 consecutive days, 50 clear images were selected and cut into 6 sub-images, and then the training set and test set were obtained by using LabelImg software for manual labeling. Then, 10 models from the series of YOLOv5 and YOLOv8, whose training parameters were set as the same (batch size was 32, iteration was 100 rounds, initial learning rate was 0.01, and periodic learning rate was 0.01), were selected and trained by using the server of the AutoDL platform. Finally, the trained models were tested, and the YOLOv8l model showed the best overall performance, with mAP50 reaching 0.926. In order to provide users with convenient and easy-to-use man-machine software, the front end of the software was developed by using PYQT5, to realize the functions of reading and counting of cotton aphid pictures, visualizing results and exporting results to Excel. The back end of the software adopted an image processing method of "splitting-detection-merging", which ensuring the efficient detection of YOLO model on small targets. After testing, the software had an average precision of 0.945 for counting dead cotton aphids and live cotton aphids, which was comparable to manual counting and had good practical value. This research may provide an intelligent detection tool for researchers related to cotton aphid control and also provided key operation information for precise operation in scenes such as unmanned farms.

cotton aphid  /  counting  /  image recognition  /  image processing  /  YOLO
马盼, 杨子恒, 万虎, 何顺, 黄远, 徐胜勇. 基于YOLOv8网络的棉蚜图像识别算法及软件系统设计. 智能化农业装备学报(中英文), 2023 , 4 (3) : 42 -49 . DOI: 10.12398/j.issn.2096-7217.2023.03.005
Pan MA, Ziheng YANG, Hu WAN, Shun HE, Yuan HUANG, Shengyong XU. A new cotton aphid image recognition algorithm and software based on YOLOv8[J]. Journal of Intelligent Agricultural Mechanization, 2023 , 4 (3) : 42 -49 . DOI: 10.12398/j.issn.2096-7217.2023.03.005
Year 2023 volume 4 Issue 3
28
8
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Article Info
doi: 10.12398/j.issn.2096-7217.2023.03.005
  • Receive Date:2023-05-31
  • Online Date:2026-11-05
  • Published:2023-08-15
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  • Received:2023-05-31
  • Revised:2023-08-03
Funding
Affiliations
    1.College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
    2.Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shenzhen 518000, China
    3.Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518000, China
    4.College of Plant Science & Technology of Huazhong Agricultural University, Wuhan 430070, China
    5.College of Horticulture & Forestry Sciences of Huazhong Agricultural University, Wuhan 430070, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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