Using computer vision to monitor germination time course of sunflower (Helianthus annuus L.) seeds

Ducournau, Sylvie; Feutry, A.; Poulichet, Patrick; Revollon, P.; Vigouroux, Bertrand; Wagner, Marie-Hélène

doi:10.15258/sst.2005.33.2.06

Cited by 32 publications

(29 citation statements)

References 12 publications

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“…The lines were phenotyped for germination using the automated PHENOSEM platform at GEVES (Angers, France; Ducournau et al, 2004Ducournau et al, , 2005Wagner et al, 2012;Demilly et al, 2014). Based on seed germination characteristics (mean germination time, germination rates at 36 and 72 h, time taken to 50% germination) measured at 20 • C, we selected five rapeseed inbred lines: Groß Lüsewitzer, Jupiter, Lipton, Savannah and SW Gospel.…”

Section: Plant Materials and Laboratory Measurementsmentioning

confidence: 99%

Virtual modeling based on deep phenotyping provides complementary data to field experiments to predict plant emergence in oilseed rape genotypes

Dürr

Constantin²,

Wagner³

et al. 2016

European Journal of Agronomy

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Section: Plant Materials and Laboratory Measurementsmentioning

confidence: 99%

Virtual modeling based on deep phenotyping provides complementary data to field experiments to predict plant emergence in oilseed rape genotypes

Dürr

Constantin²,

Wagner³

et al. 2016

European Journal of Agronomy

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“…Computer imaging is the technique attracting most attention (Dell'Aquila, 2007), including the assessment of the first signs of germination (Ducournau et al, 2005). Such methods need to be tested on commercially available seeds in several laboratories before they can be regarded as fit for purpose internationally.…”

Section: International Standardizationmentioning

confidence: 99%

Evaluation of seed quality: from physiology to international standardization

et al. 2012

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Seed quality standards enable seed users to achieve their objectives in the establishment of uniform seedlings to a high and reliable level for a range of agricultural and horticultural crops, growing systems and market outlets. Quality standards of commercial seed lots are determined by their positions on the seed survival curves and the shape of their germination progress curves. Although comparative descriptions of germination curves can be achieved by the calculation of the mean germination time (MGT; delay to radicle emergence), single early counts of radicle emergence provide a convenient means of predicting MGT and differences between seed lots. Evidence is presented for an ageing and metabolic repair hypothesis as the overall physiological basis to explain the principles behind the standard germination and vigour tests (ageing, electrolyte leakage, cold test, germination rate and seedling size). The work of the International Seed Testing Association (ISTA) in developing convenient, inexpensive and internationally repeatable tests is illustrated.

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“…(2007), the setup suggested by these authors only allows a limited number of samples. Also the system with a camera above a Jacobsen table for Helianthus annuus seeds as described by Ducournau et al. (2005) does not accomodate high‐throughput screens without expensive robotics, as it requires proper alignment between two consecutive images.…”

Section: Introductionmentioning

confidence: 99%

germinator: a software package for high-throughput scoring and curve fitting of Arabidopsis seed germination

Joosen¹,

Kodde

Willems³

et al. 2009

The Plant Journal

277

272

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SUMMARYOver the past few decades seed physiology research has contributed to many important scientific discoveries and has provided valuable tools for the production of high quality seeds. An important instrument for this type of research is the accurate quantification of germination; however gathering cumulative germination data is a very laborious task that is often prohibitive to the execution of large experiments. In this paper we present the GERMINATOR package: a simple, highly cost-efficient and flexible procedure for high-throughput automatic scoring and evaluation of germination that can be implemented without the use of complex robotics. The GERMINATOR package contains three modules: (i) design of experimental setup with various options to replicate and randomize samples; (ii) automatic scoring of germination based on the color contrast between the protruding radicle and seed coat on a single image; and (iii) curve fitting of cumulative germination data and the extraction, recap and visualization of the various germination parameters. The curve-fitting module enables analysis of general cumulative germination data and can be used for all plant species. We show that the automatic scoring system works for Arabidopsis thaliana and Brassica spp. seeds, but is likely to be applicable to other species, as well. In this paper we show the accuracy, reproducibility and flexibility of the GERMINATOR package. We have successfully applied it to evaluate natural variation for salt tolerance in a large population of recombinant inbred lines and were able to identify several quantitative trait loci for salt tolerance. GERMINATOR is a low-cost package that allows the monitoring of several thousands of germination tests, several times a day by a single person.

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Using computer vision to monitor germination time course of sunflower (Helianthus annuus L.) seeds

Cited by 32 publications

References 12 publications

Virtual modeling based on deep phenotyping provides complementary data to field experiments to predict plant emergence in oilseed rape genotypes

Virtual modeling based on deep phenotyping provides complementary data to field experiments to predict plant emergence in oilseed rape genotypes

Evaluation of seed quality: from physiology to international standardization

germinator: a software package for high-throughput scoring and curve fitting of Arabidopsis seed germination

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