Raspberry Pi–powered imaging for plant phenotyping

Premise of the Study Image‐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 ca...

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Bibliographic Details
Published in:Applications in plant sciences Vol. 6; no. 3; pp. e1031 - n/a
Main Authors: Tovar, Jose C., Hoyer, J. Steen, Lin, Andy, Tielking, Allison, Callen, Steven T., Elizabeth Castillo, S., Miller, Michael, Tessman, Monica, Fahlgren, Noah, Carrington, James C., Nusinow, Dmitri A., Gehan, Malia A.
Format: Journal Article
Language:English
Published: United States John Wiley & Sons, Inc 01.03.2018
John Wiley and Sons Inc
Subjects:
Cameras
color
computer software
Computers
Data analysis
Documentation
Image processing
imaging
low‐cost phenotyping
Metadata
morphology
phenomics
phenotype
Phenotyping
Plant extracts
Plant sciences
Protocol
Protocol Note
Protocol Notes
raspberries
Raspberry Pi
Research programs
Shutdowns
Software
species diversity
New York
United States > US
low‐cost phenotyping
Raspberry Pi
morphology
imaging
ISSN:2168-0450, 2168-0450
Online Access:Get full text
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Summary:Premise of the Study Image‐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 Results We 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. Conclusions This 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.
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These authors contributed equally.
ISSN:2168-0450
2168-0450
DOI:10.1002/aps3.1031