Quantification of the three-dimensional root system architecture using an automated rotating imaging system

Plant Methods

Methodology

Yuyang Yao¹  Yan Guo²  Yuntao Ma²  Kun Yu³  Huayong Li³  Hongxin Cao⁴  Jing Cao⁴  Qian Wu⁴  Wenyu Zhang⁵  Weixin Zhang⁵  Baiming Li⁵  Jie Wu⁶  Pengcheng Hu⁷ 

[1] College of Electronics & Information Engineering, Nanjing University of Information Science and Technology, 210044, Nanjing, Jiangsu, China;College of Land Science and Technology, China Agricultural University, 100193, Beijing, China;IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, Institute of Germplasm Resources and Biotechnology, Jiangsu Academy of Agricultural Sciences, 210014, Nanjing, Jiangsu, China;IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, YuanQi-IAI Joint Laboratory for Agricultural Digital Twin, Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, 210014, Nanjing, Jiangsu, China;IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, YuanQi-IAI Joint Laboratory for Agricultural Digital Twin, Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, 210014, Nanjing, Jiangsu, China;School of Agricultural Engineering, Jiangsu University, 212013, Zhenjiang, Jiangsu, China;Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, 210095, Nanjing, Jiangsu, China;School of Agriculture and Food Sciences, The University of Queensland, 4072, St. Lucia, QLD, Australia;

关键词: Automated imaging;    Multi-view stereo;    3D root phenotyping;    Global/local root trait;    Root segmentation;    Initial root angle;   

DOI  :  10.1186/s13007-023-00988-1

 received in 2022-07-22, accepted in 2023-01-24,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundCrop breeding based on root system architecture (RSA) optimization is an essential factor for improving crop production in developing countries. Identification, evaluation, and selection of root traits of soil-grown crops require innovations that enable high-throughput and accurate quantification of three-dimensional (3D) RSA of crops over developmental time.ResultsWe proposed an automated imaging system and 3D imaging data processing pipeline to quantify the 3D RSA of soil-grown individual plants across seedlings to the mature stage. A multi-view automated imaging system composed of a rotary table and an imaging arm with 12 cameras mounted with a combination of fan-shaped and vertical distribution was developed to obtain 3D image data of roots grown on a customized root support mesh. A 3D imaging data processing pipeline was developed to quantify the 3D RSA based on the point cloud generated from multi-view images. The global architecture of root systems can be quantified automatically. Detailed analysis of the reconstructed 3D root model also allowed us to investigate the Spatio-temporal distribution of roots. A method combining horizontal slicing and iterative erosion and dilation was developed to automatically segment different root types, and identify local root traits (e.g., length, diameter of the main root, and length, diameter, initial angle, and the number of nodal roots or lateral roots). One maize (Zea mays L.) cultivar and two rapeseed (Brassica napus L.) cultivars at different growth stages were selected to test the performance of the automated imaging system and 3D imaging data processing pipeline.ConclusionsThe results demonstrated the capabilities of the proposed imaging and analytical system for high-throughput phenotyping of root traits for both monocotyledons and dicotyledons across growth stages. The proposed system offers a potential tool to further explore the 3D RSA for improving root traits and agronomic qualities of crops.

【 授权许可】

CC BY   
© The Author(s) 2023

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