CRE-YOLO: Efficient Jaboticaba Tree Detection on UAV Platforms
This study focuses on the detection of Jaboticaba trees in an orchard located in Nanxiong City, Guangdong Province, utilizing UAV platforms to enhance precision agriculture practices. The primary objective is to compress the parameters of deep learning models while improving accuracy to enable their...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10807194/ |
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| _version_ | 1841563379530989568 |
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| author | Junyu Huang Renbo Luo Yuna Tan Zhuowen Wu |
| author_facet | Junyu Huang Renbo Luo Yuna Tan Zhuowen Wu |
| author_sort | Junyu Huang |
| collection | DOAJ |
| description | This study focuses on the detection of Jaboticaba trees in an orchard located in Nanxiong City, Guangdong Province, utilizing UAV platforms to enhance precision agriculture practices. The primary objective is to compress the parameters of deep learning models while improving accuracy to enable their deployment on UAV platforms for rapid Jaboticaba tree identification. The proposed CRE-YOLO model integrates a Cross-Scale Feature Fusion Module (CCFM), the RepDWBlock, and an Efficient Channel Attention (ECA) mechanism, reducing model parameters by 54%, effectively decreasing model complexity while improving detection precision. CRE-YOLO achieves a mean average precision (mAP) of 97.1% at IoU 0.5 and 60.3% at IoU 0.95, with a processing speed of 387 frames per second. Field experiments conducted in the research area identified over 13,000 Jaboticaba trees, demonstrating the model’s potential for practical UAV-based orchard management. The study contributes to the modernization of agricultural practices by providing an efficient, scalable solution for rapid and accurate tree detection. Future work will extend the model’s application to other crops and focus on enhancing its generalization capabilities. |
| format | Article |
| id | doaj-art-9363cb2d51d74526a000a6499caf051e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9363cb2d51d74526a000a6499caf051e2025-01-03T00:01:32ZengIEEEIEEE Access2169-35362025-01-011391692410.1109/ACCESS.2024.352011510807194CRE-YOLO: Efficient Jaboticaba Tree Detection on UAV PlatformsJunyu Huang0https://orcid.org/0009-0004-1817-8218Renbo Luo1https://orcid.org/0000-0002-4262-5506Yuna Tan2https://orcid.org/0009-0006-8894-332XZhuowen Wu3https://orcid.org/0009-0003-0634-3389School of Geography and Remote Sensing, Guangzhou University, Guangzhou, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou, ChinaThis study focuses on the detection of Jaboticaba trees in an orchard located in Nanxiong City, Guangdong Province, utilizing UAV platforms to enhance precision agriculture practices. The primary objective is to compress the parameters of deep learning models while improving accuracy to enable their deployment on UAV platforms for rapid Jaboticaba tree identification. The proposed CRE-YOLO model integrates a Cross-Scale Feature Fusion Module (CCFM), the RepDWBlock, and an Efficient Channel Attention (ECA) mechanism, reducing model parameters by 54%, effectively decreasing model complexity while improving detection precision. CRE-YOLO achieves a mean average precision (mAP) of 97.1% at IoU 0.5 and 60.3% at IoU 0.95, with a processing speed of 387 frames per second. Field experiments conducted in the research area identified over 13,000 Jaboticaba trees, demonstrating the model’s potential for practical UAV-based orchard management. The study contributes to the modernization of agricultural practices by providing an efficient, scalable solution for rapid and accurate tree detection. Future work will extend the model’s application to other crops and focus on enhancing its generalization capabilities.https://ieeexplore.ieee.org/document/10807194/UAVJaboticabaYOLOattention mechanismsmart agriculture |
| spellingShingle | Junyu Huang Renbo Luo Yuna Tan Zhuowen Wu CRE-YOLO: Efficient Jaboticaba Tree Detection on UAV Platforms IEEE Access UAV Jaboticaba YOLO attention mechanism smart agriculture |
| title | CRE-YOLO: Efficient Jaboticaba Tree Detection on UAV Platforms |
| title_full | CRE-YOLO: Efficient Jaboticaba Tree Detection on UAV Platforms |
| title_fullStr | CRE-YOLO: Efficient Jaboticaba Tree Detection on UAV Platforms |
| title_full_unstemmed | CRE-YOLO: Efficient Jaboticaba Tree Detection on UAV Platforms |
| title_short | CRE-YOLO: Efficient Jaboticaba Tree Detection on UAV Platforms |
| title_sort | cre yolo efficient jaboticaba tree detection on uav platforms |
| topic | UAV Jaboticaba YOLO attention mechanism smart agriculture |
| url | https://ieeexplore.ieee.org/document/10807194/ |
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