Seedling Stage Corn Line Detection Method Based on Improved YOLOv8
[Objective]Crop line extraction is critical for improving the efficiency of autonomous agricultural machines in the field. However, traditional detection methods struggle to maintain high accuracy and efficiency under challenging conditions, such as strong light exposure and weed interference. The a...
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Editorial Office of Smart Agriculture
2024-11-01
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| Series: | 智慧农业 |
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| Online Access: | https://www.smartag.net.cn/CN/rich_html/10.12133/j.smartag.SA202408008 |
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| author | LI Hongbo TIAN Xin RUAN Zhiwen LIU Shaowen REN Weiqi SU Zhongbin GAO Rui KONG Qingming |
| author_facet | LI Hongbo TIAN Xin RUAN Zhiwen LIU Shaowen REN Weiqi SU Zhongbin GAO Rui KONG Qingming |
| author_sort | LI Hongbo |
| collection | DOAJ |
| description | [Objective]Crop line extraction is critical for improving the efficiency of autonomous agricultural machines in the field. However, traditional detection methods struggle to maintain high accuracy and efficiency under challenging conditions, such as strong light exposure and weed interference. The aims are to develop an effective crop line extraction method by combining YOLOv8-G, Affinity Propagation, and the Least Squares method to enhance detection accuracy and performance in complex field environments.[Methods]The proposed method employs machine vision techniques to address common field challenges. YOLOv8-G, an improved object detection algorithm that combines YOLOv8 and GhostNetV2 for lightweight, high-speed performance, was used to detect the central points of crops. These points were then clustered using the Affinity Propagation algorithm, followed by the application of the Least Squares method to extract the crop lines. Comparative tests were conducted to evaluate multiple backbone networks within the YOLOv8 framework, and ablation studies were performed to validate the enhancements made in YOLOv8-G.[Results and Discussions]The performance of the proposed method was compared with classical object detection and clustering algorithms. The YOLOv8-G algorithm achieved average precision (AP) values of 98.22%, 98.15%, and 97.32% for corn detection at 7, 14, and 21 days after emergence, respectively. Additionally, the crop line extraction accuracy across all stages was 96.52%. These results demonstrate the model's ability to maintain high detection accuracy despite challenging conditions in the field.[Conclusions]The proposed crop line extraction method effectively addresses field challenges such as lighting and weed interference, enabling rapid and accurate crop identification. This approach supports the automatic navigation of agricultural machinery, offering significant improvements in the precision and efficiency of field operations. |
| format | Article |
| id | doaj-art-2e8b5d2d255b4bbda9627de742a4b2f5 |
| institution | Kabale University |
| issn | 2096-8094 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Editorial Office of Smart Agriculture |
| record_format | Article |
| series | 智慧农业 |
| spelling | doaj-art-2e8b5d2d255b4bbda9627de742a4b2f52025-01-16T15:52:14ZengEditorial Office of Smart Agriculture智慧农业2096-80942024-11-0166728410.12133/j.smartag.SA202408008SA202408008Seedling Stage Corn Line Detection Method Based on Improved YOLOv8LI Hongbo0TIAN Xin1RUAN Zhiwen2LIU Shaowen3REN Weiqi4SU Zhongbin5GAO Rui6KONG Qingming7Institutions of Electrical and Information, Northeast Agricultural University, Harbin150030, ChinaInstitutions of Electrical and Information, Northeast Agricultural University, Harbin150030, ChinaInstitutions of Electrical and Information, Northeast Agricultural University, Harbin150030, ChinaInstitutions of Electrical and Information, Northeast Agricultural University, Harbin150030, ChinaInstitutions of Electrical and Information, Northeast Agricultural University, Harbin150030, ChinaInstitutions of Electrical and Information, Northeast Agricultural University, Harbin150030, ChinaInstitutions of Electrical and Information, Northeast Agricultural University, Harbin150030, ChinaInstitutions of Electrical and Information, Northeast Agricultural University, Harbin150030, China[Objective]Crop line extraction is critical for improving the efficiency of autonomous agricultural machines in the field. However, traditional detection methods struggle to maintain high accuracy and efficiency under challenging conditions, such as strong light exposure and weed interference. The aims are to develop an effective crop line extraction method by combining YOLOv8-G, Affinity Propagation, and the Least Squares method to enhance detection accuracy and performance in complex field environments.[Methods]The proposed method employs machine vision techniques to address common field challenges. YOLOv8-G, an improved object detection algorithm that combines YOLOv8 and GhostNetV2 for lightweight, high-speed performance, was used to detect the central points of crops. These points were then clustered using the Affinity Propagation algorithm, followed by the application of the Least Squares method to extract the crop lines. Comparative tests were conducted to evaluate multiple backbone networks within the YOLOv8 framework, and ablation studies were performed to validate the enhancements made in YOLOv8-G.[Results and Discussions]The performance of the proposed method was compared with classical object detection and clustering algorithms. The YOLOv8-G algorithm achieved average precision (AP) values of 98.22%, 98.15%, and 97.32% for corn detection at 7, 14, and 21 days after emergence, respectively. Additionally, the crop line extraction accuracy across all stages was 96.52%. These results demonstrate the model's ability to maintain high detection accuracy despite challenging conditions in the field.[Conclusions]The proposed crop line extraction method effectively addresses field challenges such as lighting and weed interference, enabling rapid and accurate crop identification. This approach supports the automatic navigation of agricultural machinery, offering significant improvements in the precision and efficiency of field operations.https://www.smartag.net.cn/CN/rich_html/10.12133/j.smartag.SA202408008crop row detectionyolov8-gbackboneaffinity propagationleast square method |
| spellingShingle | LI Hongbo TIAN Xin RUAN Zhiwen LIU Shaowen REN Weiqi SU Zhongbin GAO Rui KONG Qingming Seedling Stage Corn Line Detection Method Based on Improved YOLOv8 智慧农业 crop row detection yolov8-g backbone affinity propagation least square method |
| title | Seedling Stage Corn Line Detection Method Based on Improved YOLOv8 |
| title_full | Seedling Stage Corn Line Detection Method Based on Improved YOLOv8 |
| title_fullStr | Seedling Stage Corn Line Detection Method Based on Improved YOLOv8 |
| title_full_unstemmed | Seedling Stage Corn Line Detection Method Based on Improved YOLOv8 |
| title_short | Seedling Stage Corn Line Detection Method Based on Improved YOLOv8 |
| title_sort | seedling stage corn line detection method based on improved yolov8 |
| topic | crop row detection yolov8-g backbone affinity propagation least square method |
| url | https://www.smartag.net.cn/CN/rich_html/10.12133/j.smartag.SA202408008 |
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