Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses
Open Access
- 3 July 2021
- journal article
- research article
- Published by Wiley in The Plant Journal
- Vol. 107 (6), 1837-1853
- https://doi.org/10.1111/tpj.15401
Abstract
Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments are typically performed in a low-throughput manner, such as on petri plates. Additionally, the BR pathway affects drought responses, but drought experiments are time-consuming and difficult to control. To mitigate these issues and increase throughput, we developed the Robotic Assay for Drought (RoAD) system to perform BR and drought response experiments in soil-grown Arabidopsis plants. RoAD is equipped with a robotic arm, a rover, a bench scale, a precisely controlled watering system, an RGB camera, and a laser profilometer. It performs daily weighing, watering, and imaging tasks and is capable of administering BR response assays by watering plants with Propiconazole (PCZ), a BR biosynthesis inhibitor. We developed image processing algorithms for both plant segmentation and phenotypic trait extraction to accurately measure traits including plant area, plant volume, leaf length, and leaf width. We then applied machine learning algorithms that utilized the extracted phenotypic parameters to identify image-derived traits that can distinguish control, drought, and PCZ-treated plants. We carried out PCZ and drought experiments on a set of BR mutants and Arabidopsis accessions with altered BR responses. Finally, we extended the RoAD assays to perform BR response assays using PCZ in Zea mays (maize) plants. This study establishes an automated and non-invasive robotic imaging system as a tool to accurately measure morphological and growth-related traits of Arabidopsis and maize plants in three dimensions (3D), providing insights into the BR-mediated control of plant growth and stress responses.Keywords
This publication has 75 references indexed in Scilit:
- Image-based plant phenotyping with incremental learning and active contoursEcological Informatics, 2014
- High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysisPlant Methods, 2013
- Propiconazole Is a Specific and Accessible Brassinosteroid (BR) Biosynthesis Inhibitor for Arabidopsis and MaizePLOS ONE, 2012
- Brassinosteroid control of sex determination in maizeProceedings of the National Academy of Sciences of the United States of America, 2011
- Survival and growth of Arabidopsis plants given limited water are not equalNature Biotechnology, 2011
- A brassinosteroid transcriptional network revealed by genome‐wide identification of BESI target genes in Arabidopsis thalianaThe Plant Journal, 2010
- Integration of Brassinosteroid Signal Transduction with the Transcription Network for Plant Growth Regulation in ArabidopsisDevelopmental Cell, 2010
- PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficitNew Phytologist, 2005
- Automatic quantification of morphological traits via three-dimensional measurement ofArabidopsisThe Plant Journal, 2004
- Brassinosteroids Rescue the Deficiency of CYP90, a Cytochrome P450, Controlling Cell Elongation and De-etiolation in ArabidopsisCell, 1996