OrchardQuant-3D: Combining drone and LiDAR to perform scalable 3D phenotyping for characterising key canopy and floral traits in fruit orchards
Orchard fruits such as pear and apple are important for ensuring global food security and agricultural economy as they not only provide essential nutrients, but also support biodiversity and ecosystem services. Breeders, growers and plant researchers constantly study desirable tree morphological fea...
| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/177937 |
| _version_ | 1855530259480313856 |
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| author | Xia, Yunpeng Li, Hanghang Zhang, Fanhang Sun, Gang Qi, Kaijie Jackson, Robert Pinheiro, Felipe Liu, Xiaoman Mu, Yue Zhang, Shaoling Deakin, Greg Whitfield, E. Charles Tao, Shutian Zhou, Ji |
| author_browse | Deakin, Greg Jackson, Robert Li, Hanghang Liu, Xiaoman Mu, Yue Pinheiro, Felipe Qi, Kaijie Sun, Gang Tao, Shutian Whitfield, E. Charles Xia, Yunpeng Zhang, Fanhang Zhang, Shaoling Zhou, Ji |
| author_facet | Xia, Yunpeng Li, Hanghang Zhang, Fanhang Sun, Gang Qi, Kaijie Jackson, Robert Pinheiro, Felipe Liu, Xiaoman Mu, Yue Zhang, Shaoling Deakin, Greg Whitfield, E. Charles Tao, Shutian Zhou, Ji |
| author_sort | Xia, Yunpeng |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Orchard fruits such as pear and apple are important for ensuring global food security and agricultural economy as they not only provide essential nutrients, but also support biodiversity and ecosystem services. Breeders, growers and plant researchers constantly study desirable tree morphological features and floral characteristics to ensure fruit production and quality. Still, traditional orchard phenotyping is often laborious, limited in scale and prone-to-error, resulting in many attempts to develop reliable and scalable toolkits to address this challenge. Here, we present OrchardQuant-3D, an analytic pipeline for automating tree-level analysis of key canopy and floral traits for different types of fruit orchards. We first built a data fusion algorithm to register 3D point clouds collected by both drones (for colour signals) and Light Detection And Ranging (LiDAR, for precise spatial properties), reconstructing high-quality 3D orchard models at different growth stages. Then, we utilised precise global navigation satellite system signals to position trees in orchards with millimetre-level accuracy, enabling tree-level analysis of key canopy (e.g. crown volume and the number or branches) and floral traits (e.g. blossom clusters and volumes) using 3D computer vision, complex graph theory and feature engineering techniques. Equipped with the OrchardQuant-3D pipeline, we successfully measured varietal differences of four pear cultivars from a small pear orchard in Nanjing China, followed by a scale-up study that surveyed 3D tree morphologies, key floral and fruit traits from 1104 apple trees in an orchard in East Malling, United Kingdom. To the best of our knowledge, such a multi-source, comprehensive and expandable methodology has not yet been introduced to this important research domain. Hence, we believe that our work demonstrates a step change in our ability to conduct scalable 3D orchard phenotyping, which is highly valuable to advance orchard breeding, precise tree management and orchard research greatly to sustain fruit tree production in a rapidly changing climate. |
| format | Journal Article |
| id | CGSpace177937 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace1779372025-11-14T20:14:51Z OrchardQuant-3D: Combining drone and LiDAR to perform scalable 3D phenotyping for characterising key canopy and floral traits in fruit orchards Xia, Yunpeng Li, Hanghang Zhang, Fanhang Sun, Gang Qi, Kaijie Jackson, Robert Pinheiro, Felipe Liu, Xiaoman Mu, Yue Zhang, Shaoling Deakin, Greg Whitfield, E. Charles Tao, Shutian Zhou, Ji unmanned aerial vehicles phenotyping fruit trees canopy Orchard fruits such as pear and apple are important for ensuring global food security and agricultural economy as they not only provide essential nutrients, but also support biodiversity and ecosystem services. Breeders, growers and plant researchers constantly study desirable tree morphological features and floral characteristics to ensure fruit production and quality. Still, traditional orchard phenotyping is often laborious, limited in scale and prone-to-error, resulting in many attempts to develop reliable and scalable toolkits to address this challenge. Here, we present OrchardQuant-3D, an analytic pipeline for automating tree-level analysis of key canopy and floral traits for different types of fruit orchards. We first built a data fusion algorithm to register 3D point clouds collected by both drones (for colour signals) and Light Detection And Ranging (LiDAR, for precise spatial properties), reconstructing high-quality 3D orchard models at different growth stages. Then, we utilised precise global navigation satellite system signals to position trees in orchards with millimetre-level accuracy, enabling tree-level analysis of key canopy (e.g. crown volume and the number or branches) and floral traits (e.g. blossom clusters and volumes) using 3D computer vision, complex graph theory and feature engineering techniques. Equipped with the OrchardQuant-3D pipeline, we successfully measured varietal differences of four pear cultivars from a small pear orchard in Nanjing China, followed by a scale-up study that surveyed 3D tree morphologies, key floral and fruit traits from 1104 apple trees in an orchard in East Malling, United Kingdom. To the best of our knowledge, such a multi-source, comprehensive and expandable methodology has not yet been introduced to this important research domain. Hence, we believe that our work demonstrates a step change in our ability to conduct scalable 3D orchard phenotyping, which is highly valuable to advance orchard breeding, precise tree management and orchard research greatly to sustain fruit tree production in a rapidly changing climate. 2025-11 2025-11-14T20:14:50Z 2025-11-14T20:14:50Z Journal Article https://hdl.handle.net/10568/177937 en Open Access Wiley Xia, Yunpeng; Li, Hanghang; Zhang, Fanhang; Sun, Gang; Qi, Kaijie; et al. 2025. OrchardQuant-3D: Combining drone and LiDAR to perform scalable 3D phenotyping for characterising key canopy and floral traits in fruit orchards. Plant Biotechnology Journal 23(11): 4681-5350. https://doi.org/10.1111/pbi.70229 |
| spellingShingle | unmanned aerial vehicles phenotyping fruit trees canopy Xia, Yunpeng Li, Hanghang Zhang, Fanhang Sun, Gang Qi, Kaijie Jackson, Robert Pinheiro, Felipe Liu, Xiaoman Mu, Yue Zhang, Shaoling Deakin, Greg Whitfield, E. Charles Tao, Shutian Zhou, Ji OrchardQuant-3D: Combining drone and LiDAR to perform scalable 3D phenotyping for characterising key canopy and floral traits in fruit orchards |
| title | OrchardQuant-3D: Combining drone and LiDAR to perform scalable 3D phenotyping for characterising key canopy and floral traits in fruit orchards |
| title_full | OrchardQuant-3D: Combining drone and LiDAR to perform scalable 3D phenotyping for characterising key canopy and floral traits in fruit orchards |
| title_fullStr | OrchardQuant-3D: Combining drone and LiDAR to perform scalable 3D phenotyping for characterising key canopy and floral traits in fruit orchards |
| title_full_unstemmed | OrchardQuant-3D: Combining drone and LiDAR to perform scalable 3D phenotyping for characterising key canopy and floral traits in fruit orchards |
| title_short | OrchardQuant-3D: Combining drone and LiDAR to perform scalable 3D phenotyping for characterising key canopy and floral traits in fruit orchards |
| title_sort | orchardquant 3d combining drone and lidar to perform scalable 3d phenotyping for characterising key canopy and floral traits in fruit orchards |
| topic | unmanned aerial vehicles phenotyping fruit trees canopy |
| url | https://hdl.handle.net/10568/177937 |
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