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 lolasalinas.com 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 click here 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.