1
|
Walter J, Edwards J, Cai J, McDonald G, Miklavcic SJ, Kuchel H. High-Throughput Field Imaging and Basic Image Analysis in a Wheat Breeding Programme. Front Plant Sci 2019; 10:449. [PMID: 31105715 PMCID: PMC6492763 DOI: 10.3389/fpls.2019.00449] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 03/25/2019] [Indexed: 05/18/2023]
Abstract
Visual assessment of colour-based traits plays a key role within field-crop breeding programmes, though the process is subjective and time-consuming. Digital image analysis has previously been investigated as an objective alternative to visual assessment for a limited number of traits, showing suitability and slight improvement to throughput over visual assessment. However, easily adoptable, field-based high-throughput methods are still lacking. The aim of the current study was to produce a high-throughput digital imaging and analysis pipeline for the assessment of colour-based traits within a wheat breeding programme. This was achieved through the steps of (i) a proof-of-concept study demonstrating basic image analysis methods in a greenhouse, (ii) application of these methods to field trials using hand-held imaging, and (iii) developing a field-based high-throughput imaging infrastructure for data collection. The proof of concept study showed a strong correlation (r = 0.95) between visual and digital assessments of wheat physiological yellowing (PY) in a greenhouse environment, with both scores having similar heritability (H2 = 0.85 and 0.76, respectively). Digital assessment of hand-held field images showed strong correlations to visual scores for PY (r = 0.61 and 0.78), senescence (r = 0.74 and 0.75) and Septoria tritici blotch (STB; r = 0.76), with greater heritability of digital scores, excluding STB. Development of the high-throughput imaging infrastructure allowed for images of field plots to be collected at a rate of 7,400 plots per hour. Images of an advanced breeding trial collected with this system were analysed for canopy cover at two time-points, with digital scores correlating strongly to visual scores (r = 0.88 and 0.86) and having similar or greater heritability. This study details how high-throughput digital phenotyping can be applied to colour-based traits within field trials of a wheat breeding programme. It discusses the logistics of implementing such systems with minimal disruption to the programme, provides a detailed methodology for the basic image analysis methods utilized, and has potential for application to other field-crop breeding or research programmes.
Collapse
Affiliation(s)
- James Walter
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA, Australia
- Australian Grain Technologies Pty Ltd., Roseworthy, SA, Australia
| | - James Edwards
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA, Australia
- Australian Grain Technologies Pty Ltd., Roseworthy, SA, Australia
| | - Jinhai Cai
- Phenomics and Bioinformatics Research Centre, School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, Australia
| | - Glenn McDonald
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA, Australia
| | - Stanley J. Miklavcic
- Phenomics and Bioinformatics Research Centre, School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, Australia
| | - Haydn Kuchel
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA, Australia
- Australian Grain Technologies Pty Ltd., Roseworthy, SA, Australia
| |
Collapse
|
2
|
Walter J, Edwards J, Cai J, McDonald G, Miklavcic SJ, Kuchel H. High-Throughput Field Imaging and Basic Image Analysis in a Wheat Breeding Programme. Front Plant Sci 2019; 10:449. [PMID: 31105715 DOI: 10.3389/fpls.2019.00449/full] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 03/25/2019] [Indexed: 05/24/2023]
Abstract
Visual assessment of colour-based traits plays a key role within field-crop breeding programmes, though the process is subjective and time-consuming. Digital image analysis has previously been investigated as an objective alternative to visual assessment for a limited number of traits, showing suitability and slight improvement to throughput over visual assessment. However, easily adoptable, field-based high-throughput methods are still lacking. The aim of the current study was to produce a high-throughput digital imaging and analysis pipeline for the assessment of colour-based traits within a wheat breeding programme. This was achieved through the steps of (i) a proof-of-concept study demonstrating basic image analysis methods in a greenhouse, (ii) application of these methods to field trials using hand-held imaging, and (iii) developing a field-based high-throughput imaging infrastructure for data collection. The proof of concept study showed a strong correlation (r = 0.95) between visual and digital assessments of wheat physiological yellowing (PY) in a greenhouse environment, with both scores having similar heritability (H2 = 0.85 and 0.76, respectively). Digital assessment of hand-held field images showed strong correlations to visual scores for PY (r = 0.61 and 0.78), senescence (r = 0.74 and 0.75) and Septoria tritici blotch (STB; r = 0.76), with greater heritability of digital scores, excluding STB. Development of the high-throughput imaging infrastructure allowed for images of field plots to be collected at a rate of 7,400 plots per hour. Images of an advanced breeding trial collected with this system were analysed for canopy cover at two time-points, with digital scores correlating strongly to visual scores (r = 0.88 and 0.86) and having similar or greater heritability. This study details how high-throughput digital phenotyping can be applied to colour-based traits within field trials of a wheat breeding programme. It discusses the logistics of implementing such systems with minimal disruption to the programme, provides a detailed methodology for the basic image analysis methods utilized, and has potential for application to other field-crop breeding or research programmes.
Collapse
Affiliation(s)
- James Walter
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA, Australia
- Australian Grain Technologies Pty Ltd., Roseworthy, SA, Australia
| | - James Edwards
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA, Australia
- Australian Grain Technologies Pty Ltd., Roseworthy, SA, Australia
| | - Jinhai Cai
- Phenomics and Bioinformatics Research Centre, School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, Australia
| | - Glenn McDonald
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA, Australia
| | - Stanley J Miklavcic
- Phenomics and Bioinformatics Research Centre, School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, Australia
| | - Haydn Kuchel
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA, Australia
- Australian Grain Technologies Pty Ltd., Roseworthy, SA, Australia
| |
Collapse
|