Raspberry Pi-powered imaging for plant phenotyping

被引:55
|
作者
Tovar, Jose C. [1 ]
Hoyer, J. Steen [1 ,2 ]
Lin, Andy [1 ]
Tielking, Allison [1 ]
Callen, Steven T. [1 ]
Castillo, S. Elizabeth [1 ]
Miller, Michael [1 ]
Tessman, Monica [1 ]
Fahlgren, Noah [1 ]
Carrington, James C. [1 ]
Nusinow, Dmitri A. [1 ]
Gehan, Malia A. [1 ]
机构
[1] Donald Danforth Plant Sci Ctr, 975 North Warson Rd, St Louis, MO 63132 USA
[2] Washington Univ, Computat & Syst Biol Program, One Brookings Dr, St Louis, MO 63130 USA
来源
APPLICATIONS IN PLANT SCIENCES | 2018年 / 6卷 / 03期
基金
美国国家科学基金会;
关键词
imaging; low-cost phenotyping; morphology; Raspberry Pi; ARABIDOPSIS-THALIANA; PLATFORM; RESPONSES; REVEALS; GROWTH; SYSTEM;
D O I
10.1002/aps3.1031
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Premise of the StudyImage-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data. Methods and ResultsWe used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi-controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (e.g., shape, area, height, color) en masse using open-source image processing software such as PlantCV. ConclusionsThis protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.
引用
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页数:12
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