YOLO-IAPs: A Rapid Detection Method for Invasive Alien Plants in the Wild Based on Improved YOLOv9
Invasive alien plants (IAPs) present a significant threat to ecosystems and agricultural production, necessitating rigorous monitoring and detection for effective management and control. To realize accurate and rapid detection of invasive alien plants in the wild, we proposed a rapid detection appro...
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MDPI AG
2024-12-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/14/12/2201 |
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| author | Yiqi Huang Hongtao Huang Feng Qin Ying Chen Jianghua Zou Bo Liu Zaiyuan Li Conghui Liu Fanghao Wan Wanqiang Qian Xi Qiao |
| author_facet | Yiqi Huang Hongtao Huang Feng Qin Ying Chen Jianghua Zou Bo Liu Zaiyuan Li Conghui Liu Fanghao Wan Wanqiang Qian Xi Qiao |
| author_sort | Yiqi Huang |
| collection | DOAJ |
| description | Invasive alien plants (IAPs) present a significant threat to ecosystems and agricultural production, necessitating rigorous monitoring and detection for effective management and control. To realize accurate and rapid detection of invasive alien plants in the wild, we proposed a rapid detection approach grounded in an advanced YOLOv9, referred to as YOLO-IAPs, which incorporated several key enhancements to YOLOv9, including replacing the down-sampling layers in the model’s backbone with a DynamicConv module, integrating a Triplet Attention mechanism into the model, and replacing the original CIoU with the MPDloU. These targeted enhancements collectively resulted in a substantial improvement in the model’s accuracy and robustness. Extensive training and testing on a self-constructed dataset demonstrated that the proposed model achieved an accuracy of 90.7%, with the corresponding recall, mAP50, and mAP50:95 measured at 84.3%, 91.2%, and 65.1%, and a detection speed of 72 FPS. Compared to the baseline, the proposed model showed increases of 0.2% in precision, 3.5% in recall, and 1.0% in mAP50. Additionally, YOLO-IAPs outperformed other state-of-the-art object detection models, including YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv10 series, Faster R-CNN, SSD, CenterNet, and RetinaNet, demonstrating superior detection capabilities. Ablation studies further confirmed that the proposed model was effective, contributing to the overall improvement in performance, which underscored its pre-eminence in the domain of invasive alien plant detection and offered a marked improvement in detection accuracy over traditional methodologies. The findings suggest that the proposed approach has the potential to advance the technological landscape of invasive plant monitoring. |
| format | Article |
| id | doaj-art-dac91837a42d4caea59b119d1c1d933c |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-dac91837a42d4caea59b119d1c1d933c2024-12-27T14:03:00ZengMDPI AGAgriculture2077-04722024-12-011412220110.3390/agriculture14122201YOLO-IAPs: A Rapid Detection Method for Invasive Alien Plants in the Wild Based on Improved YOLOv9Yiqi Huang0Hongtao Huang1Feng Qin2Ying Chen3Jianghua Zou4Bo Liu5Zaiyuan Li6Conghui Liu7Fanghao Wan8Wanqiang Qian9Xi Qiao10College of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaCollege of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaCollege of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaCollege of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaShenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, ChinaShenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, ChinaShenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, ChinaShenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, ChinaShenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, ChinaShenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, ChinaShenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, ChinaInvasive alien plants (IAPs) present a significant threat to ecosystems and agricultural production, necessitating rigorous monitoring and detection for effective management and control. To realize accurate and rapid detection of invasive alien plants in the wild, we proposed a rapid detection approach grounded in an advanced YOLOv9, referred to as YOLO-IAPs, which incorporated several key enhancements to YOLOv9, including replacing the down-sampling layers in the model’s backbone with a DynamicConv module, integrating a Triplet Attention mechanism into the model, and replacing the original CIoU with the MPDloU. These targeted enhancements collectively resulted in a substantial improvement in the model’s accuracy and robustness. Extensive training and testing on a self-constructed dataset demonstrated that the proposed model achieved an accuracy of 90.7%, with the corresponding recall, mAP50, and mAP50:95 measured at 84.3%, 91.2%, and 65.1%, and a detection speed of 72 FPS. Compared to the baseline, the proposed model showed increases of 0.2% in precision, 3.5% in recall, and 1.0% in mAP50. Additionally, YOLO-IAPs outperformed other state-of-the-art object detection models, including YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv10 series, Faster R-CNN, SSD, CenterNet, and RetinaNet, demonstrating superior detection capabilities. Ablation studies further confirmed that the proposed model was effective, contributing to the overall improvement in performance, which underscored its pre-eminence in the domain of invasive alien plant detection and offered a marked improvement in detection accuracy over traditional methodologies. The findings suggest that the proposed approach has the potential to advance the technological landscape of invasive plant monitoring.https://www.mdpi.com/2077-0472/14/12/2201invasive alien plantsdeep learningplant identificationYOLO model |
| spellingShingle | Yiqi Huang Hongtao Huang Feng Qin Ying Chen Jianghua Zou Bo Liu Zaiyuan Li Conghui Liu Fanghao Wan Wanqiang Qian Xi Qiao YOLO-IAPs: A Rapid Detection Method for Invasive Alien Plants in the Wild Based on Improved YOLOv9 Agriculture invasive alien plants deep learning plant identification YOLO model |
| title | YOLO-IAPs: A Rapid Detection Method for Invasive Alien Plants in the Wild Based on Improved YOLOv9 |
| title_full | YOLO-IAPs: A Rapid Detection Method for Invasive Alien Plants in the Wild Based on Improved YOLOv9 |
| title_fullStr | YOLO-IAPs: A Rapid Detection Method for Invasive Alien Plants in the Wild Based on Improved YOLOv9 |
| title_full_unstemmed | YOLO-IAPs: A Rapid Detection Method for Invasive Alien Plants in the Wild Based on Improved YOLOv9 |
| title_short | YOLO-IAPs: A Rapid Detection Method for Invasive Alien Plants in the Wild Based on Improved YOLOv9 |
| title_sort | yolo iaps a rapid detection method for invasive alien plants in the wild based on improved yolov9 |
| topic | invasive alien plants deep learning plant identification YOLO model |
| url | https://www.mdpi.com/2077-0472/14/12/2201 |
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