Niu Xiaoyun, Song Zhaoying, Xu Cong, Wu Haoran, Luan Qifu, Jiang Jingmin, Li Yanjie
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.
Plant Phenomics. 2023;5:0028. doi: 10.34133/plantphenomics.0028. Epub 2023 Mar 15.
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 values of 0.60 and 0.65 on the validation set (20%), respectively. The heritability ( ) of all traits in 11 months ranged from 0 to 0.49, with the highest 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.
叶片氮（N）含量和非结构性碳水化合物（NSC）含量是反映树木生长状态的两个重要生理指标。对这两个性状进行多时间尺度的快速准确测量，能够动态监测树木生长并高效进行树木育种选择。传统的监测氮和NSC的方法耗时较长，大多用于小规模研究，且不可重复。本文于2021年的11个月期间，对383棵湿地松的树冠氮和NSC含量估计，评估了无人机多光谱成像的性能。比较了四种机器学习方法，以生成氮和NSC预测的最优模型。此外，还评估了氮和NSC遗传变异的时间尺度。结果表明，梯度提升机模型对氮和NSC的预测结果最佳，在验证集（20%）上的 值分别为0.60和0.65。11个月内所有性状的遗传力（ ）范围为0至0.49，7月和3月氮和NSC的遗传力最高（分别为0.26和0.49）。最后，选择了5个具有高氮和NSC育种值的家系。据我们所知，这是第一项使用时间序列无人机多光谱成像预测树木中氮和NSC含量，并沿时间尺度估计氮和NSC遗传变异的研究，为育种计划中家系的整体表现提供了更可靠的信息。
