RootBot: High-throughput root stress phenotyping robot

被引:1
|
作者
Ruppel, Mia [1 ]
Nelson, Sven K. [2 ,3 ]
Sidberry, Grace [4 ]
Mitchell, Madison [4 ]
Kick, Daniel [3 ]
Thomas, Shawn K. [5 ]
Guill, Katherine E. [4 ]
Oliver, Melvin J. [4 ]
Washburn, Jacob D. [3 ,6 ]
机构
[1] Univ Missouri, Dept Biomed Biol & Chem Engn, Columbia, MO USA
[2] Heliponix LLC, Plant Sci, Evansville, IN USA
[3] USDA ARS, Plant Genet Res Unit, Columbia, MO USA
[4] Univ Missouri, Div Plant Sci & Technol, Columbia, MO USA
[5] Univ Missouri, Div Biol Sci, Columbia, MO USA
[6] Univ Missouri, Plant Genet ResearchUnit, USDA ARS, 302-A Curtis Hall, Columbia, MO 65211 USA
基金
美国农业部;
关键词
automation; drought stress; phenotyping; roots; ARCHITECTURE; PLATFORM; GROWTH;
D O I
10.1002/aps3.11541
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Premise: Higher temperatures across the globe are causing an increase in the frequency and severity of droughts. In agricultural crops, this results in reduced yields, financial losses, and increased food costs at the supermarket. Root growth maintenance in drying soils plays a major role in a plant's ability to survive and perform under drought, but phenotyping root growth is extremely difficult due to roots being under the soil. Methods and Results: RootBot is an automated high-throughput phenotyping robot that eliminates many of the difficulties and reduces the time required for performing drought-stress studies on primary roots. RootBot simulates root growth conditions using transparent plates to create a gap that is filled with soil and polyethylene glycol (PEG) to simulate low soil moisture. RootBot has a gantry system with vertical slots to hold the transparent plates, which theoretically allows for evaluating more than 50 plates at a time. Software pipelines were also co-opted, developed, tested, and extensively refined for running the RootBot imaging process, storing and organizing the images, and analyzing and extracting data. Conclusions: The RootBot platform and the lessons learned from its design and testing represent a valuable resource for better understanding drought tolerance mechanisms in roots, as well as for identifying breeding and genetic engineering targets for crop plants.
引用
收藏
页数:9
相关论文
共 50 条
  • [1] High-throughput phenotyping of seminal root traits in wheat
    Cecile AI Richard
    Lee T Hickey
    Susan Fletcher
    Raeleen Jennings
    Karine Chenu
    Jack T Christopher
    Plant Methods, 11
  • [2] High-throughput phenotyping of seminal root traits in wheat
    Richard, Cecile A. I.
    Hickey, Lee T.
    Fletcher, Susan
    Jennings, Raeleen
    Chenu, Karine
    Christopher, Jack T.
    PLANT METHODS, 2015, 11
  • [3] High-throughput phenotyping
    Natalie de Souza
    Nature Methods, 2010, 7 (1) : 36 - 36
  • [4] High-throughput phenotyping
    Gehan, Malia A.
    Kellogg, Elizabeth A.
    AMERICAN JOURNAL OF BOTANY, 2017, 104 (04) : 505 - 508
  • [5] High-throughput phenotyping
    de Souza, Natalie
    NATURE METHODS, 2010, 7 (01) : 36 - 36
  • [6] Machine Learning for High-Throughput Stress Phenotyping in Plants
    Singh, Arti
    Ganapathysubramanian, Baskar
    Singh, Asheesh Kumar
    Sarkar, Soumik
    TRENDS IN PLANT SCIENCE, 2016, 21 (02) : 110 - 124
  • [7] High-throughput mouse phenotyping
    Gates, Hilary
    Mallon, Ann-Marie
    Brown, Steve D. M.
    METHODS, 2011, 53 (04) : 394 - 404
  • [8] High-throughput phenotyping of wheat seminal root traits in a breeding context
    Richard, Cecile
    Hickey, Lee
    Fletcher, Susan
    Chenu, Karine
    Borrell, Andrew
    Christopher, Jack
    AGRICULTURE AND CLIMATE CHANGE - ADAPTING CROPS TO INCREASED UNCERTAINTY (AGRI 2015), 2015, 29 : 102 - 103
  • [9] High-throughput and automatic structural and developmental root phenotyping on Arabidopsis seedlings
    Romain Fernandez
    Amandine Crabos
    Morgan Maillard
    Philippe Nacry
    Christophe Pradal
    Plant Methods, 18
  • [10] High-throughput and automatic structural and developmental root phenotyping on Arabidopsis seedlings
    Fernandez, Romain
    Crabos, Amandine
    Maillard, Morgan
    Nacry, Philippe
    Pradal, Christophe
    PLANT METHODS, 2022, 18 (01)