Evaluating Plant Drought Resistance with a Raspberry Pi and Time-lapse Photography.

Content Provider	Europe PMC
Author	Ginzburg, Daniel N. Rhee, Seung Y.
Copyright Year	2023
Description	Identifying genetic variations or treatments that confer greater resistance to drought is paramount to ensuring sustainable crop productivity. Accurate and reproducible measurement of drought stress symptoms can be achieved via automated, image-based phenotyping. Many phenotyping platforms are either cost-prohibitive, require specific technical expertise, or are simply more complex than necessary to effectively evaluate drought resistance. Certain mutations, allelic variations, or treatments result in plants that constitutively use less water. To accurately identify genetic differences or treatments that confer a drought phenotype, plants from all experimental groups must be subjected to equal levels of drought stress. This can be easily achieved by growing and imaging plants that are grown in the same pot. Here, we provide a detailed protocol to configure a Raspberry Pi computer and camera module to image seedlings of multiple genotypes growing in shared pots and to transfer images and metadata via the cloud for downstream analyses. Also detailed is a method to calculate percent soil water content of pots while being imaged to allow for comparison of stress symptoms with water availability. This protocol was recently used to uncouple differential water usage from drought resistance in a dwarf Arabidopsis thaliana mutant chiquita1-1/cost1 compared to the wild-type control. It is cost effective, suitable for any plant species, customizable to various biological questions, and requires no prior experience with electronics or basic software programming.
Abstract	Identifying genetic variations or treatments that confer greater resistance to drought is paramount to ensuring sustainable crop productivity. Accurate and reproducible measurement of drought stress symptoms can be achieved via automated, image-based phenotyping. Many phenotyping platforms are either cost-prohibitive, require specific technical expertise, or are simply more complex than necessary to effectively evaluate drought resistance. Certain mutations, allelic variations, or treatments result in plants that constitutively use less water. To accurately identify genetic differences or treatments that confer a drought phenotype, plants from all experimental groups must be subjected to equal levels of drought stress. This can be easily achieved by growing and imaging plants that are grown in the same pot. Here, we provide a detailed protocol to configure a Raspberry Pi computer and camera module to image seedlings of multiple genotypes growing in shared pots and to transfer images and metadata via the cloud for downstream analyses. Also detailed is a method to calculate percent soil water content of pots while being imaged to allow for comparison of stress symptoms with water availability. This protocol was recently used to uncouple differential water usage from drought resistance in a dwarfArabidopsis thalianamutantchiquita1-1/cost1compared to the wild-type control. It is cost effective, suitable for any plant species, customizable to various biological questions, and requires no prior experience with electronics or basic software programming.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC9901466&blobtype=pdf
Volume Number	13
PubMed Central reference number	PMC9901466
Issue Number	2
PubMed reference number	36789161
Journal	Bio-protocol [Bio Protoc]
e-ISSN	23318325
DOI	10.21769/bioprotoc.4593
Language	English
Publisher	Bio-Protocol
Publisher Date	2023-01-20
Publisher Place	1075 Lorne Way, Sunnyvale, CA 94087, USA
Access Restriction	Open
Rights License	Copyright © 2023 The Authors; exclusive licensee Bio-protocol LLC.
Subject Keyword	Drought resistance Phenotyping Soil water content Raspberry Pi Time-lapse photography
Content Type	Text
Resource Type	Article
Subject	Immunology and Microbiology Neuroscience Plant Science Biochemistry, Genetics and Molecular Biology