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Tabaracci K, Bokros NT, Oduntan Y, Kunduru B, DeKold J, Mengistie E, McDonald A, Stubbs CJ, Sekhon RS, DeBolt S, Robertson DJ. Biomechanical phenotyping pipeline for stalk lodging resistance in maize. MethodsX 2024; 12:102562. [PMID: 38292308 PMCID: PMC10825676 DOI: 10.1016/j.mex.2024.102562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/08/2024] [Indexed: 02/01/2024] Open
Abstract
Stalk lodging (structural failure crops prior to harvest) significantly reduces annual yields of vital grain crops. The lack of standardized, high throughput phenotyping methods capable of quantifying biomechanical plant traits prevents comprehensive understanding of the genetic architecture of stalk lodging resistance. A phenotyping pipeline developed to enable higher throughput biomechanical measurements of plant traits related to stalk lodging is presented. The methods were developed using principles from the fields of engineering mechanics and metrology and they enable retention of plant-specific data instead of averaging data across plots as is typical in most phenotyping studies. This pipeline was specifically designed to be implemented in large experimental studies and has been used to phenotype over 40,000 maize stalks. The pipeline includes both lab- and field-based phenotyping methodologies and enables the collection of metadata. Best practices learned by implementing this pipeline over the past three years are presented. The specific instruments (including model numbers and manufacturers) that work well for these methods are presented, however comparable instruments may be used in conjunction with these methods as seen fit.•Efficient methods to measure biomechanical traits and record metadata related to stalk lodging.•Can be used in studies with large sample sizes (i.e., > 1,000).
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Affiliation(s)
- Kaitlin Tabaracci
- Department of Mechanical Engineering, University of Idaho, Moscow, ID, USA
| | - Norbert T. Bokros
- Department of Horticulture, University of Kentucky, Lexington, KY, USA
| | - Yusuf Oduntan
- Department of Mechanical Engineering, University of Idaho, Moscow, ID, USA
| | - Bharath Kunduru
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
| | - Joseph DeKold
- Department of Mechanical Engineering, University of Idaho, Moscow, ID, USA
| | - Endalkachew Mengistie
- Department of Forest, Rangeland and Fire Sciences, University of Idaho, Moscow, ID, USA
| | - Armando McDonald
- Department of Forest, Rangeland and Fire Sciences, University of Idaho, Moscow, ID, USA
| | - Christopher J. Stubbs
- School of Computer Sciences and Engineering, Fairleigh Dickinson University, Teaneck, NJ, USA
| | - Rajandeep S. Sekhon
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
| | - Seth DeBolt
- Department of Horticulture, University of Kentucky, Lexington, KY, USA
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Sayad A, Oduntan Y, Bokros N, DeBolt S, Benzecry A, Robertson DJ, Stubbs CJ. The semi-automated development of plant cell wall finite element models. Plant Methods 2023; 19:3. [PMID: 36624506 PMCID: PMC9827646 DOI: 10.1186/s13007-023-00979-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 12/31/2022] [Indexed: 06/17/2023]
Abstract
This study presents a methodology for a high-throughput digitization and quantification process of plant cell walls characterization, including the automated development of two-dimensional finite element models. Custom algorithms based on machine learning can also analyze the cellular microstructure for phenotypes such as cell size, cell wall curvature, and cell wall orientation. To demonstrate the utility of these models, a series of compound microscope images of both herbaceous and woody representatives were observed and processed. In addition, parametric analyses were performed on the resulting finite element models. Sensitivity analyses of the structural stiffness of the resulting tissue based on the cell wall elastic modulus and the cell wall thickness; demonstrated that the cell wall thickness has a three-fold larger impact of tissue stiffness than cell wall elastic modulus.
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Affiliation(s)
- Andrew Sayad
- School of Computer Sciences and Engineering, Fairleigh Dickinson University, Teaneck, NJ, USA
| | - Yusuf Oduntan
- Department of Mechanical Engineering, University of Idaho, Moscow, ID, USA
| | - Norbert Bokros
- Department of Horticulture, University of Kentucky, Lexington, KY, 40546, USA
| | - Seth DeBolt
- Department of Horticulture, University of Kentucky, Lexington, KY, 40546, USA
| | - Alice Benzecry
- Department of Biological Sciences, Fairleigh Dickinson University, Teaneck, NJ, USA
| | - Daniel J Robertson
- Department of Mechanical Engineering, University of Idaho, Moscow, ID, USA
| | - Christopher J Stubbs
- School of Computer Sciences and Engineering, Fairleigh Dickinson University, Teaneck, NJ, USA.
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