An image acquisition system for automated monitoring of the germination rate of sunflower seeds

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

doi:10.1016/j.compag.2004.04.005

Cited by 38 publications

(26 citation statements)

References 10 publications

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“…Monitoring of seed imbibition, germination, and early radicle growth was conducted under in vitro conditions using the automated phenotyping platform of the variety control office of the French national seed testing agency (Station Nationale d'Essais de Semences, Groupe d'Etude et de contrôle des Variétés et des Semences—GEVES, Angers, France). The phenotyping platform is described in detail by Ducournau et al ( 2004 , 2005 ) and Wagner et al ( 2011 ). Image acquisition, image analysis and data analysis methods are described in detail by Demilly et al ( 2014 ).…”

Section: Methodsmentioning

confidence: 99%

Genome-wide association mapping unravels the genetic control of seed germination and vigor in Brassica napus

et al. 2015

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Rapid and uniform seed germination is a crucial prerequisite for crop establishment and high yield levels in crop production. A disclosure of genetic factors contributing to adequate seed vigor would help to further increase yield potential and stability. Here we carried out a genome-wide association study in order to define genomic regions influencing seed germination and early seedling growth in oilseed rape (Brassica napus L.). A population of 248 genetically diverse winter-type B. napus accessions was genotyped with the Brassica 60k SNP Illumina genotyping array. Automated high-throughput in vitro phenotyping provided extensive data for multiple traits related to germination and early vigor, such as germination speed, absolute germination rate and radicle elongation. The data obtained indicate that seed germination and radicle growth are strongly environmentally dependent, but could nevertheless be substantially improved by genomic-based breeding. Conditions during seed production and storage were shown to have a profound effect on seed vigor, and a variable manifestation of seed dormancy appears to contribute to differences in germination performance in B. napus. Several promising positional and functional candidate genes could be identified within the genomic regions associated with germination speed, absolute germination rate, radicle growth and thousand seed weight. These include B. napus orthologs of the Arabidopsis thaliana genes SNOWY COTYLEDON 1 (SCO1), ARABIDOPSIS TWO-COMPONENT RESPONSE REGULATOR (ARR4), and ARGINYL-t-RNA PROTEIN TRANSFERASE 1 (ATE1), which have been shown previously to play a role in seed germination and seedling growth in A. thaliana.

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Section: Methodsmentioning

confidence: 99%

Genome-wide association mapping unravels the genetic control of seed germination and vigor in Brassica napus

et al. 2015

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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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“…Many researchers [1,3,4,7,8,9,10,11,12,13,14,17,19,20,21,24,26,27,29,30,32,34,35,36,37,38,41,42,43,44,45,48,49,50,51] have applied image processing for rice germination in their studies. This is because using image processing is rapid, economic, consistent, and objective.…”

Section: Related Workmentioning

confidence: 99%

Rice Seed Germination Analysis

Lurstwut¹,

Pornpanomchai²

2016

IJCATR

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This research aimed to develop the computer software called "Rice Seed Germination Analysis (RiSGA)" which could predict rice seed image for rice germination by using an image processing technique. The RiSGA consisted of five main process modules: 1) image acquisition, 2) image pre-processing, 3) feature extraction, 4) quality control analysis and 5) quality results. Six variations of Thai rice seed species (CP111, RD41, Chiang Phattalung, Sang Yod Phattalung, Phitsanulok 2 and Chai Nat 1) were used for the experiment. The RiSGA extracted three main features: 1) color, 2) morphological and 3) texture feature. The RiSGA applied four well-known techniques: 1) Euclidean Distance (ED), 2) Rule Based System (RBS), 3) Fuzzy Logic (FL) and 4) Artificial Neural Network (ANN). The RiSGA precision of ED, RBS, FL, and ANN was 87.50%, 100%, 100%, and 100%, respectively. The average access time was 4.35 seconds per image, 5.29 seconds per image, 7.04 seconds per image, and 159.65 seconds per image, respectively.

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An image acquisition system for automated monitoring of the germination rate of sunflower seeds

Cited by 38 publications

References 10 publications

Genome-wide association mapping unravels the genetic control of seed germination and vigor in Brassica napus

Genome-wide association mapping unravels the genetic control of seed germination and vigor in Brassica napus

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

Rice Seed Germination Analysis

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