Detecting and Mapping Individual Fruit Trees in Complex Natural Environments via UAV Remote Sensing and Optimized YOLOv5
The location and number of individual fruit trees (IFTs) are critical for investigations on planting areas, fruit yield predictions, and smart orchard planning and management. These data are conventionally obtained through manual and statistical investigations that require long, laborious, and costl...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10478285/ |
| _version_ | 1797217398911139840 |
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| author | Yongzhu Xiong Xiaofeng Zeng Weiqian Lai Jiawen Liao Yankui Chen Mingyong Zhu Kekun Huang |
| author_facet | Yongzhu Xiong Xiaofeng Zeng Weiqian Lai Jiawen Liao Yankui Chen Mingyong Zhu Kekun Huang |
| author_sort | Yongzhu Xiong |
| collection | DOAJ |
| description | The location and number of individual fruit trees (IFTs) are critical for investigations on planting areas, fruit yield predictions, and smart orchard planning and management. These data are conventionally obtained through manual and statistical investigations that require long, laborious, and costly efforts. Object detection models of deep learning could provide an opportunity to detect IFTs accurately, which is essential for rapidly obtaining these data and reducing human operation errors. This study proposed an approach for detecting IFTs and mapping their spatial distributions by integrating deep learning with unmanned aerial vehicle (UAV) remote sensing. UAV remote sensing was used to collect high-resolution images of fruit trees in pomelo orchards in Meizhou, South China. Based on these images, a new individual pomelo tree image sample dataset was created through manual interpretation and field investigation. The evaluation results revealed that YOLOv5s was the best model among the five YOLOv5 models (i.e., YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, whose layers, parameters, and floating-point operations all increased with the depth and width of layers) of different scales considered for optimization. Moreover, the coordinate attention (CA) optimized YOLOv5 model (YOLOv5s-CA) is the best model (named FruitNet) with the best overall accuracy for detecting IPTs among all seven attention-optimized YOLOv5 models and other state-of-the-art object detection models, such as faster R-CNN and YOLOv8s. The IPTs in the study areas were detected using FruitNet, their number and planting area were counted, and their spatial distributions were mapped based on the predicted results of the IPTs. This study suggested that our proposed approach could provide key data and technical support for smart orchard management. |
| first_indexed | 2024-04-24T12:01:14Z |
| format | Article |
| id | doaj.art-c540c945c3bc4b7292ca485ef85ce1bb |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-24T12:01:14Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-c540c945c3bc4b7292ca485ef85ce1bb2024-04-08T23:00:16ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352024-01-01177554757610.1109/JSTARS.2024.337952210478285Detecting and Mapping Individual Fruit Trees in Complex Natural Environments via UAV Remote Sensing and Optimized YOLOv5Yongzhu Xiong0https://orcid.org/0000-0002-4417-6409Xiaofeng Zeng1https://orcid.org/0009-0007-4845-6428Weiqian Lai2https://orcid.org/0009-0009-5695-4819Jiawen Liao3https://orcid.org/0009-0004-7886-3508Yankui Chen4https://orcid.org/0009-0009-5435-2742Mingyong Zhu5https://orcid.org/0000-0001-6849-1692Kekun Huang6https://orcid.org/0000-0002-0163-4182School of Geography and Tourism, Jiaying University, Meizhou, ChinaSchool of Geography and Tourism, Jiaying University, Meizhou, ChinaSchool of Geography and Tourism, Jiaying University, Meizhou, ChinaSchool of Geography and Tourism, Jiaying University, Meizhou, ChinaSchool of Geography and Tourism, Jiaying University, Meizhou, ChinaSchool of Geography and Tourism, Jiaying University, Meizhou, ChinaSchool of Mathematics, Jiaying University, Meizhou, ChinaThe location and number of individual fruit trees (IFTs) are critical for investigations on planting areas, fruit yield predictions, and smart orchard planning and management. These data are conventionally obtained through manual and statistical investigations that require long, laborious, and costly efforts. Object detection models of deep learning could provide an opportunity to detect IFTs accurately, which is essential for rapidly obtaining these data and reducing human operation errors. This study proposed an approach for detecting IFTs and mapping their spatial distributions by integrating deep learning with unmanned aerial vehicle (UAV) remote sensing. UAV remote sensing was used to collect high-resolution images of fruit trees in pomelo orchards in Meizhou, South China. Based on these images, a new individual pomelo tree image sample dataset was created through manual interpretation and field investigation. The evaluation results revealed that YOLOv5s was the best model among the five YOLOv5 models (i.e., YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, whose layers, parameters, and floating-point operations all increased with the depth and width of layers) of different scales considered for optimization. Moreover, the coordinate attention (CA) optimized YOLOv5 model (YOLOv5s-CA) is the best model (named FruitNet) with the best overall accuracy for detecting IPTs among all seven attention-optimized YOLOv5 models and other state-of-the-art object detection models, such as faster R-CNN and YOLOv8s. The IPTs in the study areas were detected using FruitNet, their number and planting area were counted, and their spatial distributions were mapped based on the predicted results of the IPTs. This study suggested that our proposed approach could provide key data and technical support for smart orchard management.https://ieeexplore.ieee.org/document/10478285/Deep learningindividual tree detectionremote sensingspatial distributionunmanned aerial vehicle (UAV) |
| spellingShingle | Yongzhu Xiong Xiaofeng Zeng Weiqian Lai Jiawen Liao Yankui Chen Mingyong Zhu Kekun Huang Detecting and Mapping Individual Fruit Trees in Complex Natural Environments via UAV Remote Sensing and Optimized YOLOv5 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning individual tree detection remote sensing spatial distribution unmanned aerial vehicle (UAV) |
| title | Detecting and Mapping Individual Fruit Trees in Complex Natural Environments via UAV Remote Sensing and Optimized YOLOv5 |
| title_full | Detecting and Mapping Individual Fruit Trees in Complex Natural Environments via UAV Remote Sensing and Optimized YOLOv5 |
| title_fullStr | Detecting and Mapping Individual Fruit Trees in Complex Natural Environments via UAV Remote Sensing and Optimized YOLOv5 |
| title_full_unstemmed | Detecting and Mapping Individual Fruit Trees in Complex Natural Environments via UAV Remote Sensing and Optimized YOLOv5 |
| title_short | Detecting and Mapping Individual Fruit Trees in Complex Natural Environments via UAV Remote Sensing and Optimized YOLOv5 |
| title_sort | detecting and mapping individual fruit trees in complex natural environments via uav remote sensing and optimized yolov5 |
| topic | Deep learning individual tree detection remote sensing spatial distribution unmanned aerial vehicle (UAV) |
| url | https://ieeexplore.ieee.org/document/10478285/ |
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