A corn canopy organs detection method based on improved DBi-YOLOv8 network
Corn canopy organs detection is critical in obtaining high-throughput phenotypic data. Accurate identification of each organ can provide a reliable data source for canopy phenotype determination, which has significant theoretical and practical value for corn variety breeding, cultivation management,...
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| Published in: | European journal of agronomy Vol. 154; p. 127076 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.03.2024
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| ISSN: | 1161-0301, 1873-7331 |
| Online Access: | Get full text |
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| Abstract | Corn canopy organs detection is critical in obtaining high-throughput phenotypic data. Accurate identification of each organ can provide a reliable data source for canopy phenotype determination, which has significant theoretical and practical value for corn variety breeding, cultivation management, and high-quality and high-yielding production. Due to the difficulty in quickly identifying corn canopy organs in the natural environment of the field, it is challenging to obtain high-throughput phenotypic data. Therefore, this paper proposed a method for corn canopy organs detection based on an improved network model (DBi-YOLOv8). Firstly, the Raspberry Pi 4B was used as the sensor control center to construct an embedded system for corn canopy image acquisition and collected 987 images of corn plants. Secondly, the improved deformable convolution and Bi-level routing attention were embedded into the backbone and neck structures of the YOLOv8 network. With training the improved network, a corn canopy detection model was obtained, which enabled the rapid detection of corn canopy organs. Finally, the LTNS algorithm and TBC algorithm were proposed for counting of the number of leaves, ears, and tassels. On the testing set data, the detection performance of the model was analyzed through different evaluation metrics. The results showed that the mAP and FPS of the detection model were 89.4% and 65.3, which increased by 12% and 0.6 compared to the original model. In addition, both algorithms have high reliability, with the coefficient of determination R2 for counting crown leaves, ears, and tassel branches being 0.9336, 0.8149, and 0.917, respectively. This achievement proposed an accurate, non-destructive, and fast corn canopy organs detection model, providing reliable technical support for quantifying various traits of corn plants, field crop growth monitoring, and elite variety breeding.
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•Developed a platform for corn canopy image acquisition based on Raspberry Pi and SV02.•Built the DBi-YOLOv8 model for corn canopy detection based on DConv and BRA module.•The LTNS algorithm and TBC algorithm were proposed for counting corn canopy organs.•Successfully applied the improved model to field corn canopy fast detection.•Provided complete technical support for field crop phenotype acquisition. |
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| AbstractList | Corn canopy organs detection is critical in obtaining high-throughput phenotypic data. Accurate identification of each organ can provide a reliable data source for canopy phenotype determination, which has significant theoretical and practical value for corn variety breeding, cultivation management, and high-quality and high-yielding production. Due to the difficulty in quickly identifying corn canopy organs in the natural environment of the field, it is challenging to obtain high-throughput phenotypic data. Therefore, this paper proposed a method for corn canopy organs detection based on an improved network model (DBi-YOLOv8). Firstly, the Raspberry Pi 4B was used as the sensor control center to construct an embedded system for corn canopy image acquisition and collected 987 images of corn plants. Secondly, the improved deformable convolution and Bi-level routing attention were embedded into the backbone and neck structures of the YOLOv8 network. With training the improved network, a corn canopy detection model was obtained, which enabled the rapid detection of corn canopy organs. Finally, the LTNS algorithm and TBC algorithm were proposed for counting of the number of leaves, ears, and tassels. On the testing set data, the detection performance of the model was analyzed through different evaluation metrics. The results showed that the mAP and FPS of the detection model were 89.4% and 65.3, which increased by 12% and 0.6 compared to the original model. In addition, both algorithms have high reliability, with the coefficient of determination R² for counting crown leaves, ears, and tassel branches being 0.9336, 0.8149, and 0.917, respectively. This achievement proposed an accurate, non-destructive, and fast corn canopy organs detection model, providing reliable technical support for quantifying various traits of corn plants, field crop growth monitoring, and elite variety breeding. Corn canopy organs detection is critical in obtaining high-throughput phenotypic data. Accurate identification of each organ can provide a reliable data source for canopy phenotype determination, which has significant theoretical and practical value for corn variety breeding, cultivation management, and high-quality and high-yielding production. Due to the difficulty in quickly identifying corn canopy organs in the natural environment of the field, it is challenging to obtain high-throughput phenotypic data. Therefore, this paper proposed a method for corn canopy organs detection based on an improved network model (DBi-YOLOv8). Firstly, the Raspberry Pi 4B was used as the sensor control center to construct an embedded system for corn canopy image acquisition and collected 987 images of corn plants. Secondly, the improved deformable convolution and Bi-level routing attention were embedded into the backbone and neck structures of the YOLOv8 network. With training the improved network, a corn canopy detection model was obtained, which enabled the rapid detection of corn canopy organs. Finally, the LTNS algorithm and TBC algorithm were proposed for counting of the number of leaves, ears, and tassels. On the testing set data, the detection performance of the model was analyzed through different evaluation metrics. The results showed that the mAP and FPS of the detection model were 89.4% and 65.3, which increased by 12% and 0.6 compared to the original model. In addition, both algorithms have high reliability, with the coefficient of determination R2 for counting crown leaves, ears, and tassel branches being 0.9336, 0.8149, and 0.917, respectively. This achievement proposed an accurate, non-destructive, and fast corn canopy organs detection model, providing reliable technical support for quantifying various traits of corn plants, field crop growth monitoring, and elite variety breeding. [Display omitted] •Developed a platform for corn canopy image acquisition based on Raspberry Pi and SV02.•Built the DBi-YOLOv8 model for corn canopy detection based on DConv and BRA module.•The LTNS algorithm and TBC algorithm were proposed for counting corn canopy organs.•Successfully applied the improved model to field corn canopy fast detection.•Provided complete technical support for field crop phenotype acquisition. |
| ArticleNumber | 127076 |
| Author | Zhou, Haichao Lu, Yuxin Zhu, Tianyu Guan, Haiou Ma, Xiaodan Gu, Zhicheng Zhang, Yifei Deng, Haotian Zhang, Tao |
| Author_xml | – sequence: 1 givenname: Haiou surname: Guan fullname: Guan, Haiou email: gho@cau.edu.cn organization: College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, DaQing 163319, China – sequence: 2 givenname: Haotian surname: Deng fullname: Deng, Haotian email: bynd_dht@163.com organization: College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, DaQing 163319, China – sequence: 3 givenname: Xiaodan surname: Ma fullname: Ma, Xiaodan email: mxd@cau.edu.cn organization: College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, DaQing 163319, China – sequence: 4 givenname: Tao surname: Zhang fullname: Zhang, Tao email: bynd_zt@163.com organization: College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, DaQing 163319, China – sequence: 5 givenname: Yifei surname: Zhang fullname: Zhang, Yifei email: zhangyifei@byau.edu.cn organization: College of Agriculture, Heilongjiang Bayi Agricultural University, DaQing 163319, China – sequence: 6 givenname: Tianyu surname: Zhu fullname: Zhu, Tianyu email: bynd_zty@163.com organization: College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, DaQing 163319, China – sequence: 7 givenname: Haichao surname: Zhou fullname: Zhou, Haichao email: bynd_zhc@163.com organization: College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, DaQing 163319, China – sequence: 8 givenname: Zhicheng surname: Gu fullname: Gu, Zhicheng email: 2284908311@qq.com organization: College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, DaQing 163319, China – sequence: 9 givenname: Yuxin surname: Lu fullname: Lu, Yuxin email: byndlyx@163.com organization: College of Agriculture, Heilongjiang Bayi Agricultural University, DaQing 163319, China |
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| Keywords | Detection model Bi-level routing attention Improved deformable convolution YOLOv8 Corn canopy |
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| SubjectTerms | agronomy algorithms Bi-level routing attention canopy corn Corn canopy Detection model field crops Improved deformable convolution model validation neck phenotype rapid methods raspberries YOLOv8 |
| Title | A corn canopy organs detection method based on improved DBi-YOLOv8 network |
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