Unsupervised Plot-Scale LAI Phenotyping via UAV-Based Imaging, Modelling, and Machine Learning

作     者：Qiaomin Chen Bangyou Zheng Karine Chenu Pengcheng Hu Scott C.Chapman Qiaomin Chen;Bangyou Zheng;Karine Chenu;Pengcheng Hu;Scott C. Chapman

作者机构：School of Agriculture and Food SciencesThe University of QueenslandSt LuciaQLDAustralia Agriculture and FoodCSIROQueensland Bioscience PrecinctSt LuciaQLDAustralia The University of QueenslandQueensland Alliance for Agriculture and Food InnovationToowoombaQLDAustralia 

出 版 物：《Plant Phenomics》 (植物表型组学（英文）)

年 卷 期：2022年第4卷第1期

页      面：196-214页

核心收录：

学科分类：12[管理学] 08[工学] 0711[理学-系统科学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080203[工学-机械设计及理论] 0714[理学-统计学（可授理学、经济学学位）] 0802[工学-机械工程] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：The field experiment conducted in 2016 was supported by the Grains Research and Development Corporation(Grant no.CSP00179) results from this work inform a related project(UOQ2003-011RTX) 

主　　题：breeding forest estimation 

摘      要：High-throughput phenotyping has become the frontier to accelerate breeding through linking genetics to crop growth estimation,which requires accurate estimation of leaf area index(LAI).This study developed a hybrid method to train the random forest regression(RFR)models with synthetic datasets generated by a radiative transfer model to estimate LAI from UAV-based multispectral images.