An unmanned ground vehicle phenotyping-based method to generate three-dimensional multispectral point clouds for deciphering spatial heterogeneity in plant traits  

作　　者：Pengyao Xie Zhihong Ma Ruiming Du Xin Yang Yu Jiang Haiyan Cen 

机构地区：[1]College of Biosystems Engineering and Food Science,Zhejiang University,Hangzhou 310058,China [2]Key Laboratory of Spectroscopy Sensing,Ministry of Agriculture and Rural Affairs,Hangzhou 310058,China [3]Horticulture Section,School of Integrative Plant Science,Cornell University,Geneva,NY 14456,USA

出　　处：《Molecular Plant》2024年第10期1624-1638,共15页分子植物（英文版）

基　　金：funded by the National Natural Science Foundation of China(32371985);the Fundamental Research Funds for the Central Universities,China(226-2022-00217).

摘　　要：Fusing three-dimensional(3D)and multispectral(MS)imaging data holds promise for high-throughput and comprehensive plant phenotyping to decipher genome-to-phenome knowledge.Acquiring high-quality 3D MS point clouds(3DMPCs)of plants remains challenging because of poor 3D data quality and limited radiometric calibration methods for plants with a complex canopy structure.Here,we present a novel 3D spatial–spectral data fusion approach to collect high-quality 3DMPCs of plants by integrating the next-best-view planning for adaptive data acquisition and neural reference field(NeREF)for radiometric calibration.This approach was used to acquire 3DMPCs of perilla,tomato,and rapeseed plants with diverse plant architecture and leaf morphological features evaluated by the accuracy of chlorophyll content and equivalent water thickness(EWT)estimation.The results showed that the completeness of plant point clouds collected by this approach was improved by an average of 23.6%compared with the fixed viewpoints alone.The NeREF-based radiometric calibration with the hemispherical reference outperformed the conventional calibration method by reducing the root mean square error(RMSE)of 58.93%for extracted reflectance spectra.The RMSE for chlorophyll content and EWT predictions decreased by 21.25%and 14.13%using partial least squares regression with the generated 3DMPCs.Collectively,our study provides an effective and efficient way to collect high-quality 3DMPCs of plants under natural light conditions,which improves the accuracy and comprehensiveness of phenotyping plant morphological and physiological traits,and thus will facilitate plant biology and genetic studies as well as crop breeding.

关 键 词：adaptive data acquisition three-dimensional multispectral point clouds radiometric calibration plant phenotyping chlorophyll content equivalent water thickness 

分 类 号：Q94[生物学—植物学]