An instrumental set up for seed germination studies with temperature control and automatic image recording

Teixeira, Pedro Celso Nogueira; Neto, José A. Coelho; Rocha, Humberto; Oliveira, Juracy M. de

doi:10.1590/s1677-04202007000200002

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…In this type of large experiments it is difficult to manually score germination at multiple time points per day for a number of days or weeks. Although major progress for semi‐automated scoring of seed germination of Lactuca sativa , using a flat‐bed scanner, was made by Teixeira et al. (2007), the setup suggested by these authors only allows a limited number of samples.…”

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

271

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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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

271

272

View full text Add to dashboard Cite

show abstract

“…These systems can generate high quality images at a high temporal resolution, and can be scaled by increasing the number of scanners. However, precise light control during growth is challenging and the cost of scaling may be prohibitive (Slovak et al 2014; Teixeira et al 2007). Teixeira et al 2007 demonstrated humidity and temperature regulation, but not light control.…”

Section: Discussionmentioning

confidence: 99%

Robotic Imaging and Machine Learning Analysis of Seed Germination: Dissecting the Influence of ABA and DOG1 on Germination Uniformity

Eckhardt,

Xing,

Subramanian

et al. 2024

Preprint

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Seed germination research has evolved over the years, increasingly incorporating technology. Recent advances in phenotyping platforms have increased the accessibility of high throughput phenotyping technologies to more labs, leading to valuable insights into germination biology. These platforms benefit researchers by limiting manual labor and increasing the temporal resolution of imaging. Each of the platforms developed presents unique benefits and challenges, from scalability to price to computing resources. Performing experiments involving thousands of seeds remains a daunting task due to the limitations of current phenotyping platforms and image analysis pipelines. To overcome these challenges, we introduce SPENCER (Seed Phenotype Evaluation and Germination Curve Estimation Robot), a high-throughput phenotyping platform. SPENCER accommodates 32 rectangular petri plates, capable of assessing up to 8000 Arabidopsis seeds per experiment. Our design allows for high quality images while maintaining optimal humidity, crucial for precise germination assessment over longer experiments. The image analysis workflow incorporates advanced image analysis using semantic segmentation models trained for Arabidopsis and lettuce, providing researchers with accessible, reproducible, and efficient tools. We applied SPENCER to investigate the relative roles of DELAY OF GERMINATION 1 (DOG1) and abscisic acid (ABA) in Arabidopsis dormancy. DOG1 mutants exhibited rapid germination, whereas ANT application had a greater impact on the slower-germinating Ler ecotype. Our findings suggest that DOG1 plays a significant role in dormancy, particularly in non-dormant accessions, while ABA's influence is more pronounced under stress conditions. Additionally, we explored germination uniformity, another agriculturally relevant trait, observing parallels with germination timing. SPENCER offers a powerful and accessible tool for dissecting complex biological traits in conjunction with chemical and genetic manipulations. Its scalability and versatility make it suitable for large-scale genetic and chemical germination screens.

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“…In particular, many CV and ML combined analytic methods have been developed to enable the automation of organ‐level phenotypic analysis, including leaves, roots, and reproductive organs (Pound et al ., 2017; Sadeghi‐Tehran et al ., 2017; Xiong et al ., 2017; Zhou et al ., 2017a; Yasrab et al ., 2019). By combining colour, texture, morphologies, and growth patterns, seed germination can be quantified in a dynamic and objective manner, based on which large‐scale and reproducible evidence can be produced to enable new biological discoveries for seed physiology (Teixeira et al ., 2007; Demilly et al ., 2015; Reyazul et al ., 2015; Lurstwut & Pornpanomchai, 2017; Elmasry et al ., 2019). Furthermore, the automation of seed germination scoring presents a good opportunity to initiate the standardisation of seed science research.…”

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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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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An instrumental set up for seed germination studies with temperature control and automatic image recording

Cited by 10 publications

References 13 publications

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

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

Robotic Imaging and Machine Learning Analysis of Seed Germination: Dissecting the Influence of ABA and DOG1 on Germination Uniformity

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

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