Evaluating sowing uniformity in hybrid rice using image processing and the OEW-YOLOv8n network
Sowing uniformity is an important evaluation indicator of mechanical sowing quality. In order to achieve accurate evaluation of sowing uniformity in hybrid rice mechanical sowing, this study takes the seeds in a seedling tray of hybrid rice blanket-seedling nursing as the research object and propose...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1473153/full |
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| author | Zehua Li Zehua Li Yihui Pan Xu Ma Yongjun Lin Xicheng Wang Hongwei Li |
| author_facet | Zehua Li Zehua Li Yihui Pan Xu Ma Yongjun Lin Xicheng Wang Hongwei Li |
| author_sort | Zehua Li |
| collection | DOAJ |
| description | Sowing uniformity is an important evaluation indicator of mechanical sowing quality. In order to achieve accurate evaluation of sowing uniformity in hybrid rice mechanical sowing, this study takes the seeds in a seedling tray of hybrid rice blanket-seedling nursing as the research object and proposes a method for evaluating sowing uniformity by combining image processing methods and the ODConv_C2f-ECA-WIoU-YOLOv8n (OEW-YOLOv8n) network. Firstly, image processing methods are used to segment seed image and obtain seed grids. Next, an improved model named OEW-YOLOv8n based on YOLOv8n is proposed to identify the number of seeds in a unit seed grid. The improved strategies include the following: (1) Replacing the Conv module in the Bottleneck of C2f modules with the Omni-Dimensional Dynamic Convolution (ODConv) module, where C2f modules are located at the connection between the Backbone and Neck. This improvement can enhance the feature extraction ability of the Backbone network, as the new modules can fully utilize the information of all dimensions of the convolutional kernel. (2) An Efficient Channel Attention (ECA) module is added to the Neck for improving the network’s capability to extract deep semantic feature information of the detection target. (3) In the Bbox module of the prediction head, the Complete Intersection over Union (CIoU) loss function is replaced by the Weighted Intersection over Union version 3 (WIoUv3) loss function to improve the convergence speed of the bounding box loss function and reduce the convergence value of the loss function. The results show that the mean average precision (mAP) of the OEW-YOLOv8n network reaches 98.6%. Compared to the original model, the mAP improved by 2.5%. Compared to the advanced object detection algorithms such as Faster-RCNN, SSD, YOLOv4, YOLOv5s YOLOv7-tiny, and YOLOv10s, the mAP of the new network increased by 5.2%, 7.8%, 4.9%, 2.8% 2.9%, and 3.3%, respectively. Finally, the actual evaluation experiment showed that the test error is from −2.43% to 2.92%, indicating that the improved network demonstrates excellent estimation accuracy. The research results can provide support for the mechanized sowing quality detection of hybrid rice and the intelligent research of rice seeder. |
| format | Article |
| id | doaj-art-0e5242806deb4d6f824ac9c5a77494b4 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Plant Science |
| spelling | doaj-art-0e5242806deb4d6f824ac9c5a77494b42025-02-03T06:33:33ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-02-011610.3389/fpls.2025.14731531473153Evaluating sowing uniformity in hybrid rice using image processing and the OEW-YOLOv8n networkZehua Li0Zehua Li1Yihui Pan2Xu Ma3Yongjun Lin4Xicheng Wang5Hongwei Li6College of Mathematics and Informatics, South China Agricultural University, Guangzhou, ChinaKey Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs, Guangzhou, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou, ChinaCollege of Engineering, South China Agricultural University, Guangzhou, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou, ChinaCollege of Engineering, South China Agricultural University, Guangzhou, ChinaSchool of Mechanical Engineering, Guangxi University, Nanning, ChinaSowing uniformity is an important evaluation indicator of mechanical sowing quality. In order to achieve accurate evaluation of sowing uniformity in hybrid rice mechanical sowing, this study takes the seeds in a seedling tray of hybrid rice blanket-seedling nursing as the research object and proposes a method for evaluating sowing uniformity by combining image processing methods and the ODConv_C2f-ECA-WIoU-YOLOv8n (OEW-YOLOv8n) network. Firstly, image processing methods are used to segment seed image and obtain seed grids. Next, an improved model named OEW-YOLOv8n based on YOLOv8n is proposed to identify the number of seeds in a unit seed grid. The improved strategies include the following: (1) Replacing the Conv module in the Bottleneck of C2f modules with the Omni-Dimensional Dynamic Convolution (ODConv) module, where C2f modules are located at the connection between the Backbone and Neck. This improvement can enhance the feature extraction ability of the Backbone network, as the new modules can fully utilize the information of all dimensions of the convolutional kernel. (2) An Efficient Channel Attention (ECA) module is added to the Neck for improving the network’s capability to extract deep semantic feature information of the detection target. (3) In the Bbox module of the prediction head, the Complete Intersection over Union (CIoU) loss function is replaced by the Weighted Intersection over Union version 3 (WIoUv3) loss function to improve the convergence speed of the bounding box loss function and reduce the convergence value of the loss function. The results show that the mean average precision (mAP) of the OEW-YOLOv8n network reaches 98.6%. Compared to the original model, the mAP improved by 2.5%. Compared to the advanced object detection algorithms such as Faster-RCNN, SSD, YOLOv4, YOLOv5s YOLOv7-tiny, and YOLOv10s, the mAP of the new network increased by 5.2%, 7.8%, 4.9%, 2.8% 2.9%, and 3.3%, respectively. Finally, the actual evaluation experiment showed that the test error is from −2.43% to 2.92%, indicating that the improved network demonstrates excellent estimation accuracy. The research results can provide support for the mechanized sowing quality detection of hybrid rice and the intelligent research of rice seeder.https://www.frontiersin.org/articles/10.3389/fpls.2025.1473153/fullmechanical sowinguniformity evaluationdeep learningobject detectionrice seeder |
| spellingShingle | Zehua Li Zehua Li Yihui Pan Xu Ma Yongjun Lin Xicheng Wang Hongwei Li Evaluating sowing uniformity in hybrid rice using image processing and the OEW-YOLOv8n network Frontiers in Plant Science mechanical sowing uniformity evaluation deep learning object detection rice seeder |
| title | Evaluating sowing uniformity in hybrid rice using image processing and the OEW-YOLOv8n network |
| title_full | Evaluating sowing uniformity in hybrid rice using image processing and the OEW-YOLOv8n network |
| title_fullStr | Evaluating sowing uniformity in hybrid rice using image processing and the OEW-YOLOv8n network |
| title_full_unstemmed | Evaluating sowing uniformity in hybrid rice using image processing and the OEW-YOLOv8n network |
| title_short | Evaluating sowing uniformity in hybrid rice using image processing and the OEW-YOLOv8n network |
| title_sort | evaluating sowing uniformity in hybrid rice using image processing and the oew yolov8n network |
| topic | mechanical sowing uniformity evaluation deep learning object detection rice seeder |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1473153/full |
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