Seed and germination testing
Seed and seedling counting, seed purity determination, seed and seedling quality assessments, seed respiration analysis, and more
High quality data on seeds and seedlings are of interest in plant breeding, seed production, seed testing labs, for seed treatments, plant variety offices, plant cultivar protection, in gene banks, and research.
Seed and seedling quality assessments require numerical data on seeds and seedlings. These include seed and seedling counts and dimensions, for instance length of emerging roots and shoots. Beyond counting and measuring, detailed assessments determine quality features, e.g. whether seedling germination is normal, or whether seed batches contain foreign seeds.
Digital imaging and advanced image processing deliver well-documented reliable data that are repeatable and can be standardized. Machine learning facilitates training the algorithms to quality criteria on seedling normality, seed purity, and many more user-defined features. The human factor that introduces bias whenever a rating is done by visual inspection is minimized, because all results are gained independently from personal impressions.
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APPLICATION CASES
SOLUTIONS
Seeds are outstandingly important – most plants propagate via seeds and for many crops, e.g. cereals and oil seeds, the seeds are the essential part of the harvest.
Seedling emergence classified
Determining the seed quality is a key step in plant research, seed breeding, seed production, seed trade, and seed storage and maintenance in gene banks. This implies the properties of the seed as such as well as germination characteristics, together with tests for purity or weed contamination in seed batches.
Many protocols are available that determine how to test the seed features, seed batch properties, seed germination, or seedling emergence. Common for all these protocols are visual inspections of the samples. Whereas general testing protocols usually are designed to allow experts to work without requiring complex technical equipment, digital seed testing tools improve and accelerate the testing process.
Wheat germination classified
Testing protocols can be institute- or company-specific, but frequently seed testers use guidelines issued by ISTA, AOSA-SCST or seed regulatory agencies. Testing protocols aim at establishing comparable seed quality determination procedures.
An important task in seed testing is the quality determination of the seedlings. For seedling emergence assays, but also for paper-based germination assays, it is not only important to know what percentage of seeds do germinate, but particularly how many of them deliver normal, usable plants. Such normal, or usable seedlings grow well and have the expected phenotypic appearance, and they do not exhibit defects, deformations, or infestations.
Digital Seed Testing
Digital seed testing tools do not change the testing process as such, but they provide an assistance and documenting system for the inspection process. By complementing the visual inspection, they improve the process so that it is better standardised, repeatable, person-independent and high-throughput.
The digital systems work with images recorded from the seed- or seedling samples. Recording images has two major goals – first, image processing extracts features that are relevant for the inspection as such, and second, images serve as documentation of the sample material at the moment of the inspection. Already the documentation via the recorded images is an advantage over the visual scoring process where numbers are noted down. The documentation allows re-inspection of the material at later times, if required. The main advantage is the feature extraction, of course. The feature extraction recognises whether a seed has germinated, a seedling has emerged, and it delivers information on the quality. The quality information can comprise shoot- and root- dimensions, geometrical measures of the seedlings or colour distributions in the seedlings.
Feature extraction can take advantage of classical image processing, but advanced machine learning is becoming more prominent recently. The machine learning processes allow to train the algorithms according to user-specific sample material and to better discriminate normal from abnormal seedlings. Thus, the identification of usable seedling is only possible by using machine learning tools.
Our digital seed testing portfolio comprises scoring of seed properties, of seed germination traits, and of seedling emergence traits.
The LemnaTec SeedAIxpert applies digital images of seeds and seedlings to detect germination, seed properties, and seedling quality. The PhenoSeeder is a high-precision seed phenotyping instrument that can do seed sorting and seed placing.
We bought a LemnaTec LabScanalyzer (the predecessor of PhenoAIxpert) in July 2018. The instrument was delivered to us and installed remotely by a Lemnatec technician that instructed us about the use of the instrument. We were happy about the service: very good communication and excellent instrument set-up. The Lemnatec LabScanalyzer works nicely and is even more user-friendly than we expected. Almost no training of new users is needed. We hope to expand the uses of the instrument to obtain additional phenotyping options in the future, by interacting with LemnaTec.