New Perspectives for Seed Germination Testing Through Digital Imaging Technology

Dell’Aquila, A.

doi:10.2174/1874331500903010037

Cited by 8 publications

(6 citation statements)

References 30 publications

(39 reference statements)

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“…New morphological traits included in the SeedGerm analysis are seed convex area, seed extent and seed circularity, which have been used to quantify dynamics of germination of different crop seeds, as they were difficult to assess using traditional approaches (TeKrony & Egli, 1991; Dell’Aquila, 2009). For example, using the seed convex area trait, we found that maize had the quickest establishment rate after T 50 , while other crop seeds were very similar (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The emergence of plant phenomics in recent years has brought new perspectives to seed science research (Dell’Aquila, 2009; Watson et al ., 2018). By combining cost‐effective digital imaging and environment sensors, organ‐level plant growth and development can be recorded with detailed imagery, at a very high frequency (Tardieu et al ., 2017; Pieruschka & Schurr, 2019; Reynolds et al ., 2019b).…”

Section: Introductionmentioning

confidence: 99%

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SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination

et al. 2020

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Efficient seed germination and establishment are important traits for field and glasshouse crops. Large-scale germination experiments are laborious and prone to observer errors, leading to the necessity for automated methods. We experimented with five crop species, including tomato, pepper, Brassica, barley, and maize, and concluded an approach for large-scale germination scoring. Here, we present the SeedGerm system, which combines cost-effective hardware and open-source software for seed germination experiments, automated seed imaging, and machine-learning based phenotypic analysis. The software can process multiple image series simultaneously and produce reliable analysis of germination-and establishment-related traits, in both comma-separated values (CSV) and processed images (PNG) formats. In this article, we describe the hardware and software design in detail. We also demonstrate that SeedGerm could match specialists' scoring of radicle emergence. Germination curves were produced based on seed-level germination timing and rates rather than a fitted curve. In particular, by scoring germination across a diverse panel of Brassica napus varieties, SeedGerm implicates a gene important in abscisic acid (ABA) signalling in seeds. We compared SeedGerm with existing methods and concluded that it could have wide utilities in large-scale seed phenotyping and testing, for both research and routine seed technology applications.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination

et al. 2020

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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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“…Classic methods of assessment, including standard germination tests, have been gradually improved. Image analysis, which is capable of detecting even the smallest changes of the seed dimensions during modified germination tests, has started to play an important part in this process (Dell' Aguila, 2005, 2006, 2009Dell' Aguila et al, 2000;Jossen et al, 2010). The application of image analysis provides much more information about the germination process.…”

Section: Introductionmentioning

confidence: 99%

Imbibition of wheat seeds: Application of image analysis

Lev¹,

Blahovec²

2017

International Agrophysics

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A b s t r a c t . Image analysis is widely used for monitoring seeds during germination, and it is often the final phase of germination that is subjected to the greatest attention. However, the initial phase of germination (the so-called imbibition) also exhibits interesting behaviour. This work shows that image analysis has significant potential in the imbibition. Herein, a total of 120 seeds were analysed during germination tests, and information about seed size and shape was stored and analysed. It was found that the imbibition can be divided into two newly defined parts. The first one ('abrupt imbibition') consists mainly of the swelling of the seed embryo part and lasts approximately one hour. The second one, referred to as 'main imbibition', consists mainly of spatial expansion caused by imbibition in the other parts of the seed. The results presented are supported by the development of seed cross area and shape parameters, and by direct observation.

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New Perspectives for Seed Germination Testing Through Digital Imaging Technology

Cited by 8 publications

References 30 publications

SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination

SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination

Rice Seed Germination Analysis

Imbibition of wheat seeds: Application of image analysis

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