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EasyDCP: An affordable, high‐throughput tool to measure plant phenotypic traits in 3D
Author(s) -
Feldman Alexander,
Wang Haozhou,
Fukano Yuya,
Kato Yoichiro,
Ninomiya Seishi,
Guo Wei
Publication year - 2021
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.13645
Subject(s) - throughput , python (programming language) , workflow , software , computer science , photogrammetry , measure (data warehouse) , ground truth , remote sensing , data mining , geography , artificial intelligence , database , operating system , wireless
High‐throughput 3D phenotyping is a rapidly emerging field that has widespread application for measurement of individual plants. Despite this, high‐throughput plant phenotyping is rarely used in ecological studies due to financial and logistical limitations. We introduce EasyDCP, a Python package for 3D phenotyping, which uses photogrammetry to automatically reconstruct 3D point clouds of individuals within populations of container plants and output phenotypic trait data. Here we give instructions for the imaging setup and the required hardware, which is minimal and do‐it‐yourself, and introduce the functionality and workflow of EasyDCP. We compared the performance of EasyDCP against a high‐end commercial laser scanner for the acquisition of plant height and projected leaf area. Both tools had strong correlations with ground truth measurement, and plant height measurements were more accurate using EasyDCP (plant height: EasyDCP r 2 = 0.96, Laser r 2 = 0.86; projected leaf area: EasyDCP r 2 = 0.96, Laser r 2 = 0.96). EasyDCP is an open‐source software tool to measure phenotypic traits of container plants with high‐throughput and low labour and financial costs.