Hauptseite > Publikationsdatenbank > phenoSeeder - A robot system for automated handling and phenotyping of individual seeds > print |
001 | 819219 | ||
005 | 20220930130105.0 | ||
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100 | 1 | _ | |a Jahnke, Siegfried |0 P:(DE-Juel1)129336 |b 0 |e Corresponding author |
245 | _ | _ | |a phenoSeeder - A robot system for automated handling and phenotyping of individual seeds |
260 | _ | _ | |a Rockville, Md. |c 2016 |b Soc. |
336 | 7 | _ | |a article |2 DRIVER |
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520 | _ | _ | |a The enormous diversity of seed traits is an intriguing feature and critical for the overwhelming success of higher plants. In particular, seed mass is generally regarded to be key for seedling development but is mostly approximated by using scanning methods delivering only two-dimensional data, often termed seed size. However, three-dimensional traits, such as the volume or mass of single seeds, are very rarely determined in routine measurements. Here, we introduce a device named phenoSeeder, which enables the handling and phenotyping of individual seeds of very different sizes. The system consists of a pick-and-place robot and a modular setup of sensors that can be versatilely extended. Basic biometric traits detected for individual seeds are two-dimensional data from projections, three-dimensional data from volumetric measures, and mass, from which seed density is also calculated. Each seed is tracked by an identifier and, after phenotyping, can be planted, sorted, or individually stored for further evaluation or processing (e.g. in routine seed-to-plant tracking pipelines). By investigating seeds of Arabidopsis (Arabidopsis thaliana), rapeseed (Brassica napus), and barley (Hordeum vulgare), we observed that, even for apparently round-shaped seeds of rapeseed, correlations between the projected area and the mass of seeds were much weaker than between volume and mass. This indicates that simple projections may not deliver good proxies for seed mass. Although throughput is limited, we expect that automated seed phenotyping on a single-seed basis can contribute valuable information for applications in a wide range of wild or crop species, including seed classification, seed sorting, and assessment of seed quality. |
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