文献类型：期刊文献

英文题名：Prediction of Needle Physiological Traits Using UAV Imagery for Breeding Selection of Slash Pine

作者：Niu, Xiaoyun[1] Song, Zhaoying[1,2] Xu, Cong[3] Wu, Haoran[1,2] Luan, Qifu[2] Jiang, Jingmin[2] Li, Yanjie[2]

第一作者：Niu, Xiaoyun

通信作者：Li, YJ[1]

机构：[1]Hebei Agr Univ, Coll Landscape Architecture & Tourism, Baoding 071000, Peoples R China;[2]Chinese Acad Forestry, Res Inst Subtrop Forestry, 73 Daqiao Rd, Hangzhou 311400, Zhejiang, Peoples R China;[3]Univ Canterbury, New Zealand Sch Forestry, Private Bag 4800, Christchurch 8041, New Zealand

年份：2023

卷号：5

外文期刊名：PLANT PHENOMICS

收录：;EI(收录号：20231513884413);Scopus(收录号：2-s2.0-85152197134);WOS:【SCI-EXPANDED(收录号:WOS:001007467500003)】；

基金：This research was supported by Fundamental Research Funds of CAF, no. CAFYBB2022QA001, and the Zhejiang Science and Technology Major Program on Agricultural New Variety Breeding, no. 2021C02070-1-1.

语种：英文

外文关键词：Antennas - Genes - Learning systems - Physiology - Unmanned aerial vehicles (UAV) - Vehicle performance

摘要：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.