Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles
Wild rice, a natural gene pool for rice germplasm innovation and variety improvement, holds immense value in rice breeding due to its disease-resistance genes. Traditional disease resistance identification in wild rice heavily relies on labor-intensive and subjective manual methods, posing significa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1256545/full |
| _version_ | 1827786637490257920 |
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| author | Pan Pan Pan Pan Wenlong Guo Xiaoming Zheng Xiaoming Zheng Lin Hu Lin Hu Guomin Zhou Guomin Zhou Guomin Zhou Jianhua Zhang Jianhua Zhang |
| author_facet | Pan Pan Pan Pan Wenlong Guo Xiaoming Zheng Xiaoming Zheng Lin Hu Lin Hu Guomin Zhou Guomin Zhou Guomin Zhou Jianhua Zhang Jianhua Zhang |
| author_sort | Pan Pan |
| collection | DOAJ |
| description | Wild rice, a natural gene pool for rice germplasm innovation and variety improvement, holds immense value in rice breeding due to its disease-resistance genes. Traditional disease resistance identification in wild rice heavily relies on labor-intensive and subjective manual methods, posing significant challenges for large-scale identification. The fusion of unmanned aerial vehicles (UAVs) and deep learning is emerging as a novel trend in intelligent disease resistance identification. Detecting diseases in field conditions is critical in intelligent disease resistance identification. In pursuit of detecting bacterial blight in wild rice within natural field conditions, this study presents the Xoo-YOLO model, a modification of the YOLOv8 model tailored for this purpose. The Xoo-YOLO model incorporates the Large Selective Kernel Network (LSKNet) into its backbone network, allowing for more effective disease detection from the perspective of UAVs. This is achieved by dynamically adjusting its large spatial receptive field. Concurrently, the neck network receives enhancements by integrating the GSConv hybrid convolution module. This addition serves to reduce both the amount of calculation and parameters. To tackle the issue of disease appearing elongated and rotated when viewed from a UAV perspective, we incorporated a rotational angle (theta dimension) into the head layer's output. This enhancement enables precise detection of bacterial blight in any direction in wild rice. The experimental results highlight the effectiveness of our proposed Xoo-YOLO model, boasting a remarkable mean average precision (mAP) of 94.95%. This outperforms other models, underscoring its superiority. Our model strikes a harmonious balance between accuracy and speed in disease detection. It is a technical cornerstone, facilitating the intelligent identification of disease resistance in wild rice on a large scale. |
| first_indexed | 2024-03-11T16:38:03Z |
| format | Article |
| id | doaj.art-5f26b966acac48088c262ca94023aba1 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-03-11T16:38:03Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-5f26b966acac48088c262ca94023aba12023-10-23T11:49:10ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-10-011410.3389/fpls.2023.12565451256545Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehiclesPan Pan0Pan Pan1Wenlong Guo2Xiaoming Zheng3Xiaoming Zheng4Lin Hu5Lin Hu6Guomin Zhou7Guomin Zhou8Guomin Zhou9Jianhua Zhang10Jianhua Zhang11National Agriculture Science Data Center, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, ChinaNational Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, ChinaInstitute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, ChinaNational Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, ChinaInstitute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, ChinaNational Agriculture Science Data Center, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, ChinaNational Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, ChinaNational Agriculture Science Data Center, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, ChinaNational Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, ChinaFarmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences, Xinxiang, ChinaNational Agriculture Science Data Center, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, ChinaNational Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, ChinaWild rice, a natural gene pool for rice germplasm innovation and variety improvement, holds immense value in rice breeding due to its disease-resistance genes. Traditional disease resistance identification in wild rice heavily relies on labor-intensive and subjective manual methods, posing significant challenges for large-scale identification. The fusion of unmanned aerial vehicles (UAVs) and deep learning is emerging as a novel trend in intelligent disease resistance identification. Detecting diseases in field conditions is critical in intelligent disease resistance identification. In pursuit of detecting bacterial blight in wild rice within natural field conditions, this study presents the Xoo-YOLO model, a modification of the YOLOv8 model tailored for this purpose. The Xoo-YOLO model incorporates the Large Selective Kernel Network (LSKNet) into its backbone network, allowing for more effective disease detection from the perspective of UAVs. This is achieved by dynamically adjusting its large spatial receptive field. Concurrently, the neck network receives enhancements by integrating the GSConv hybrid convolution module. This addition serves to reduce both the amount of calculation and parameters. To tackle the issue of disease appearing elongated and rotated when viewed from a UAV perspective, we incorporated a rotational angle (theta dimension) into the head layer's output. This enhancement enables precise detection of bacterial blight in any direction in wild rice. The experimental results highlight the effectiveness of our proposed Xoo-YOLO model, boasting a remarkable mean average precision (mAP) of 94.95%. This outperforms other models, underscoring its superiority. Our model strikes a harmonious balance between accuracy and speed in disease detection. It is a technical cornerstone, facilitating the intelligent identification of disease resistance in wild rice on a large scale.https://www.frontiersin.org/articles/10.3389/fpls.2023.1256545/fullwild riceUAVbacterial blightdisease detectiondeep learningYOLOv8 |
| spellingShingle | Pan Pan Pan Pan Wenlong Guo Xiaoming Zheng Xiaoming Zheng Lin Hu Lin Hu Guomin Zhou Guomin Zhou Guomin Zhou Jianhua Zhang Jianhua Zhang Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles Frontiers in Plant Science wild rice UAV bacterial blight disease detection deep learning YOLOv8 |
| title | Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles |
| title_full | Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles |
| title_fullStr | Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles |
| title_full_unstemmed | Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles |
| title_short | Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles |
| title_sort | xoo yolo a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles |
| topic | wild rice UAV bacterial blight disease detection deep learning YOLOv8 |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1256545/full |
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