Research Article | Open Access
Volume 2020 |Article ID 1848437 | https://doi.org/10.34133/2020/1848437

MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction

Sheng Wu,1,2,3 Weiliang WeniD ,1,2,3 Yongjian Wang,1,2,3 Jiangchuan Fan,1,2,3 Chuanyu Wang,1,2,3 Wenbo Gou,1,2,3 Xinyu Guo iD 1,2,3

1Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
2National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
3Beijing Key Lab of Digital Plant, Beijing 100097, China

Received 
06 Nov 2019
Accepted 
19 Feb 2020
Published
12 Mar 2020

Abstract

Plant phenotyping technologies play important roles in plant research and agriculture. Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation. Accordingly, high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed, and MVS-Pheno, a portable and low-cost phenotyping platform for individual plants, was developed. The platform is composed of four major components: a semiautomatic multiview stereo (MVS) image acquisition device, a data acquisition console, data processing and phenotype extraction software for maize shoots, and a data management system. The platform’s device is detachable and adjustable according to the size of the target shoot. Image sequences for each maize shoot can be captured within 60-120 seconds, yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software, and the phenotypic traits at the organ and individual plant levels are then extracted by the software. The correlation coefficient () between the extracted and manually measured plant height, leaf width, and leaf area values are 0.99, 0.87, and 0.93, respectively. A data management system has also been developed to store and manage the acquired raw data, reconstructed point clouds, agronomic information, and resulting phenotypic traits. The platform offers an optional solution for high-throughput phenotyping of field-grown plants, which is especially useful for large populations or experiments across many different ecological regions.

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