A low-cost and open-source platform for automated imaging

Remote monitoring of plants using hyperspectral imaging has become an important tool for the study of plant growth, development, and physiology. Many applications are oriented towards use in field environments to enable non-destructive analysis of crop responses due to factors such as drought, nutrient deficiency, and disease, e.g., using tram, drone, or airplane mounted instruments.
Authors: Max R. Lien, Richard J. Barker, Zhiwei Ye, Matthew H. Westphall, Ruohan Gao, Aditya Singh,Simon Gilroy, Philip A. Townsend
 
Journal Title: Plant Methods
 
ISSN: 1746-4811 (Online)
 
Publisher: BMC
 
Abstract
 
Remote monitoring of plants using hyperspectral imaging has become an important tool for the study of plant growth, development, and physiology. Many applications are oriented towards use in field environments to enable non-destructive analysis of crop responses due to factors such as drought, nutrient deficiency, and disease, e.g., using tram, drone, or airplane mounted instruments. The field setting introduces a wide range of uncontrolled environmental variables that make validation and interpretation of spectral responses challenging, and as such lab- and greenhouse-deployed systems for plant studies and phenotyping are of increasing interest. In this study, we have designed and developed an open-source, hyperspectral reflectance-based imaging system for lab-based plant experiments: the HyperScanner. The reliability and accuracy of HyperScanner were validated using drought and salt stress experiments with Arabidopsis thaliana.
 

Photo: a Representative images of wild type Col-0 Arabidopsis responding to drought and 500 mM NaCl stress. Plants were grown for 19 days, before applying stress treatments. Images were analyzed using the Phenotiki image analysis software: b Rosette perimeter, c rosette diameter, and d rosette area (b–d mean±SE, n=18 replicates). Bars represent points signifcantly diferent from control, t-test, p<0.05 (credits: Max R. Lien, Richard J. Barker, Zhiwei Ye, Matthew H. Westphall, Ruohan Gao, Aditya Singh,Simon Gilroy, Philip A. Townsend)

Availability of data and materials 
 
The datasets supporting the conclusions of this article are available in the Cyverse repository (https://de.cyverse.org/de/?type=data&folder=/iplant/home/elytas/experiment_repository).
The 3D printable model fles are available on the Harvard Dataverse (https://doi.org/10.7910/DVN/9DLR7S).
Ardupy is available on our Github (https://github.com/EnSpec/Plant_CNC_Controller) and archived on Zenodo (https://doi.org/10.5281/zenodo.1406721).
 
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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