文献类型：期刊文献

英文题名：Multitemporal UAV study of phenolic compounds in slash pine canopies

作者：Song, Zhaoying[1,3] Xu, Cong[2] Luan, Qifu[1] Li, Yanjie[1]

第一作者：Song, Zhaoying

通信作者：Li, YJ[1]

机构：[1]Chinese Acad Forestry, Res Inst Subtrop Forestry, State Key Lab Tree Genet & Breeding, Hangzhou 311400, Zhejiang, Peoples R China;[2]Univ Canterbury, Sch Forestry, Private Bag 4800, Christchurch 8140, New Zealand;[3]Hebei Agr Univ, Coll Landscape Architecture & Tourism, Baoding 071000, Peoples R China

年份：2024

卷号：315

外文期刊名：REMOTE SENSING OF ENVIRONMENT

收录：;EI(收录号：20244017139572);Scopus(收录号：2-s2.0-85205325332);WOS:【SCI-EXPANDED(收录号:WOS:001331040100001)】；

基金：This research was supported by Fundamental Research Funds of CAF, No. CAFYBB2022QA001 and the Science and Technology innovation 2030-Agricultural biological breeding major project (2023ZD040580105) .

语种：英文

外文关键词：Multitemporal analysis; Forest growth; Tree breeding; Remote sensing; Machine learning; Secondary metabolites; UAV

摘要：Phenolic compounds (PC) are important secondary metabolites in plants, playing a crucial role in plant defense mechanisms against pathogens and other plants. Monitoring PC levels is important for understanding tree stress and implementing effective breeding programs. However, traditional methods for monitoring PC are time-consuming, prone to altering the phenolic composition, and mostly applicable only on a small scale. In this study, we evaluated the performance of Unoccupied Aerial Vehicles (UAV) multispectral imaging in estimating the canopy phenolic content in slash pine over an 11-month period in 2021 and a seven-month period in 2022. Three machine learning models including Partial least squares regression (PLSR), Random forest (RF) and Support Vector Machine (SVM) were compared to determine the optimal predictive model for canopy PC. The RF model provided the best predictive results, with R-2 values of 0.82 for the validation set and 0.94 for the calibration set. Additionally, the study assesses the heritable variation in canopy PC over time, with the monthly heritability (h(2)) of PC ranging from 0 to 0.26 in 2021 and from 0 to 0.35 in 2022; The highest h(2) levels were observed in July and September 2021and July 2022. The findings demonstrate significant genetic control over the variation of PC. Furthermore, we observed higher breeding values and genetic gains in July and November, which further supports the strong correlation between PC and environmental factors such as temperature and light intensity. To the best of our knowledge, this is the first study to employ time-series UAV multispectral imaging to predict secondary metabolites in pine trees and estimate their genetic variation over time. As a proof of concept, these findings provide more reliable information for tree breeding programs, ultimately enhancing their overall performance.