Abstract
L.) improvement worldwide, desired seed-level characteristics and seed quality receive a growing attention as they directly impact early seedling establishment, seed longevity, and grain quality. Nevertheless, the throughput and accuracy of seed-level phenotyping and analysis have become a key limiting factor in this research domain, requiring new solutions to relieve this bottleneck. In this study, we first combined automated multispectral seed imaging (MSI; i.e. the VideometerLab 4 and Autofeeder systems) with a variety of machine learning and computer vision techniques to establish a high-throughput pipeline to analyse wheat seeds. Then, using 493 lines selected from the NIAB Diverse MAGIC (NDM) population, we applied the pipeline to segment individual seeds from MSI seed-lot images. This enabled us to perform seed-level measurement of sixteen morphological (e.g. seed size, length, width, and roundness) and spectral traits, ranging from ultraviolet (i.e. 375 nm, correlating with crude protein) to near-infrared (e.g. 975 nm, for assessing water content) wavelengths. After verifying these seed quality related traits (R2 ≥ 0.949; p < 0.001), we applied genome-wide association studies (GWAS) to link the computationally derived traits to genetic loci and identified eleven significant loci. Some of the loci were previously reported, with two unknown loci valuable for further assessment. Taken together, we believe this integrated MSI analysis pipeline provides a powerful solution for seed research and crop improvement in wheat, enabling us to bridge MSI, seed-level analysis, and genetic mapping to assess seed morphology, seed quality, and their underlying genetic architectures effectively.
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Claims 4
Claim · model-written summary
The pipeline measured sixteen morphological and spectral seed traits.
- Taxon
- wheat
Triticum aestivumt0 - Outcome
- sixteen morphological (e.g. seed size, length, width, and roundness) and spectral traitse0
- Value
- sixteene0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“This enabled us to perform seed-level measurement of sixteen morphological (e.g. seed size, length, width, and roundness) and spectral traits”
t0“grain traits in wheat”
Claim · model-written summary
Seed quality trait verification achieved R2 at least 0.949.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- seed quality related traitse0
- Value
- R2 ≥ 0.949e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“After verifying these seed quality related traits (R2 ≥ 0.949; p < 0.001)”
t0“grain traits in wheat”
Claim · model-written summary
GWAS identified eleven significant loci associated with computational seed traits.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- eleven significant locie0
- Value
- elevene0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“we applied genome-wide association studies (GWAS) to link the computationally derived traits to genetic loci and identified eleven significant loci.”
t0“grain traits in wheat”
Claim · model-written summary
Two of the loci were previously unknown.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- unknown locie0
- Value
- twoe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Some of the loci were previously reported, with two unknown loci valuable for further assessment.”
t0“grain traits in wheat”