Prediction of Needle Physiological Traits Using UAV Imagery for Breeding Selection of Slash Pine
Leaf nitrogen (N) content and nonstructural carbohydrate (NSC) content are 2 important physiological indicators that reflect the growth state of trees. Rapid and accurate measurement of these 2 traits multitemporally enables dynamic monitoring of tree growth and efficient tree breeding selection. Tr...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2023-01-01
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| Series: | Plant Phenomics |
| Online Access: | https://spj.science.org/doi/10.34133/plantphenomics.0028 |
| _version_ | 1797810689460404224 |
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| author | Xiaoyun Niu Zhaoying Song Cong Xu Haoran Wu Qifu Luan Jingmin Jiang Yanjie Li |
| author_facet | Xiaoyun Niu Zhaoying Song Cong Xu Haoran Wu Qifu Luan Jingmin Jiang Yanjie Li |
| author_sort | Xiaoyun Niu |
| collection | DOAJ |
| description | Leaf nitrogen (N) content and nonstructural carbohydrate (NSC) content are 2 important physiological indicators that reflect the growth state of trees. Rapid and accurate measurement of these 2 traits multitemporally enables dynamic monitoring of tree growth and efficient tree breeding selection. Traditional methods to monitor N and NSC are time-consuming, are mostly used on a small scale, and are nonrepeatable. In this paper, the performance of unmanned aerial vehicle multispectral imaging was evaluated over 11 months of 2021 on the estimation of canopy N and NSC contents from 383 slash pine trees. Four machine learning methods were compared to generate the optimal model for N and NSC prediction. In addition, the temporal scale of heritable variation for N and NSC was evaluated. The results show that the gradient boosting machine model yields the best prediction results on N and NSC, with R2 values of 0.60 and 0.65 on the validation set (20%), respectively. The heritability (h2) of all traits in 11 months ranged from 0 to 0.49, with the highest h2 for N and NSC found in July and March (0.26 and 0.49, respectively). Finally, 5 families with high N and NSC breeding values were selected. To the best of our knowledge, this is the first study to predict N and NSC contents in trees using time-series unmanned aerial vehicle multispectral imaging and estimating the genetic variation of N and NSC along a temporal scale, which provides more reliable information about the overall performance of families in a breeding program. |
| first_indexed | 2024-03-13T07:11:32Z |
| format | Article |
| id | doaj.art-1df28c6e64484c679892807df18da778 |
| institution | Directory Open Access Journal |
| issn | 2643-6515 |
| language | English |
| last_indexed | 2024-03-13T07:11:32Z |
| publishDate | 2023-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Plant Phenomics |
| spelling | doaj.art-1df28c6e64484c679892807df18da7782023-06-05T19:32:03ZengAmerican Association for the Advancement of Science (AAAS)Plant Phenomics2643-65152023-01-01510.34133/plantphenomics.0028Prediction of Needle Physiological Traits Using UAV Imagery for Breeding Selection of Slash PineXiaoyun Niu0Zhaoying Song1Cong Xu2Haoran Wu3Qifu Luan4Jingmin Jiang5Yanjie Li6College of Landscape Architecture and Tourism, Hebei Agriculture University, Baoding 071000, China.College of Landscape Architecture and Tourism, Hebei Agriculture University, Baoding 071000, China.New Zealand School of Forestry, University of Canterbury, Private Bag 4800, 8041 Christchurch, New Zealand.College of Landscape Architecture and Tourism, Hebei Agriculture University, Baoding 071000, China.Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou 311400, Zhejiang Province, China.Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou 311400, Zhejiang Province, China.Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou 311400, Zhejiang Province, China.Leaf nitrogen (N) content and nonstructural carbohydrate (NSC) content are 2 important physiological indicators that reflect the growth state of trees. Rapid and accurate measurement of these 2 traits multitemporally enables dynamic monitoring of tree growth and efficient tree breeding selection. Traditional methods to monitor N and NSC are time-consuming, are mostly used on a small scale, and are nonrepeatable. In this paper, the performance of unmanned aerial vehicle multispectral imaging was evaluated over 11 months of 2021 on the estimation of canopy N and NSC contents from 383 slash pine trees. Four machine learning methods were compared to generate the optimal model for N and NSC prediction. In addition, the temporal scale of heritable variation for N and NSC was evaluated. The results show that the gradient boosting machine model yields the best prediction results on N and NSC, with R2 values of 0.60 and 0.65 on the validation set (20%), respectively. The heritability (h2) of all traits in 11 months ranged from 0 to 0.49, with the highest h2 for N and NSC found in July and March (0.26 and 0.49, respectively). Finally, 5 families with high N and NSC breeding values were selected. To the best of our knowledge, this is the first study to predict N and NSC contents in trees using time-series unmanned aerial vehicle multispectral imaging and estimating the genetic variation of N and NSC along a temporal scale, which provides more reliable information about the overall performance of families in a breeding program.https://spj.science.org/doi/10.34133/plantphenomics.0028 |
| spellingShingle | Xiaoyun Niu Zhaoying Song Cong Xu Haoran Wu Qifu Luan Jingmin Jiang Yanjie Li Prediction of Needle Physiological Traits Using UAV Imagery for Breeding Selection of Slash Pine Plant Phenomics |
| title | Prediction of Needle Physiological Traits Using UAV Imagery for Breeding Selection of Slash Pine |
| title_full | Prediction of Needle Physiological Traits Using UAV Imagery for Breeding Selection of Slash Pine |
| title_fullStr | Prediction of Needle Physiological Traits Using UAV Imagery for Breeding Selection of Slash Pine |
| title_full_unstemmed | Prediction of Needle Physiological Traits Using UAV Imagery for Breeding Selection of Slash Pine |
| title_short | Prediction of Needle Physiological Traits Using UAV Imagery for Breeding Selection of Slash Pine |
| title_sort | prediction of needle physiological traits using uav imagery for breeding selection of slash pine |
| url | https://spj.science.org/doi/10.34133/plantphenomics.0028 |
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