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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17059979 |
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| _version_ | 1866901747589447680 |
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| author | Lin, Yida Xue, Bing Zhang, Mengjie Schofield, Sam Green, Richard |
| author_facet | Lin, Yida Xue, Bing Zhang, Mengjie Schofield, Sam Green, Richard |
| contents | <div>This research presents the development of a drone equipped with pruning tools and</div> <div>a stereo vision camera, designed to accurately detect the spatial position of tree</div> <div>branches. By utilizing stereo vision technology, the drone precisely identifies branch</div> <div>locations and executes targeted pruning operations. In response to the growing de</div> <div>mand for automation and increased efficiency in agriculture and forestry, this study</div> <div>tackles the challenge of accurately detecting and measuring the distance between the</div> <div>camera and tree branches, which is essential for precise pruning. The primary aim</div> <div>is to establish a robust methodology that enables drones to reliably identify branch</div> <div>positions and compute their spatial distances. This is accomplished through the in</div> <div>tegration of advanced computer vision techniques and machine learning algorithms.</div> <div>The research outcomes demonstrate significant improvements in the accuracy and ef</div> <div>ficiency of the drone’s camera in precisely detecting the spatial position of branches,</div> <div>underscoring the transformative potential of deep learning technologies in advancing</div> <div>automation within the agriculture and forestry sectors.</div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17059979 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Position radiata pine branches requiring pruning by drone stereo vision Lin, Yida Xue, Bing Zhang, Mengjie Schofield, Sam Green, Richard <div>This research presents the development of a drone equipped with pruning tools and</div> <div>a stereo vision camera, designed to accurately detect the spatial position of tree</div> <div>branches. By utilizing stereo vision technology, the drone precisely identifies branch</div> <div>locations and executes targeted pruning operations. In response to the growing de</div> <div>mand for automation and increased efficiency in agriculture and forestry, this study</div> <div>tackles the challenge of accurately detecting and measuring the distance between the</div> <div>camera and tree branches, which is essential for precise pruning. The primary aim</div> <div>is to establish a robust methodology that enables drones to reliably identify branch</div> <div>positions and compute their spatial distances. This is accomplished through the in</div> <div>tegration of advanced computer vision techniques and machine learning algorithms.</div> <div>The research outcomes demonstrate significant improvements in the accuracy and ef</div> <div>ficiency of the drone’s camera in precisely detecting the spatial position of branches,</div> <div>underscoring the transformative potential of deep learning technologies in advancing</div> <div>automation within the agriculture and forestry sectors.</div> |
| title | Position radiata pine branches requiring pruning by drone stereo vision |
| url | https://doi.org/10.5281/zenodo.17059979 |