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dc.contributor.authorBraguy, Justine
dc.contributor.authorRamazanova, Merey
dc.contributor.authorGiancola, Silvio
dc.contributor.authorJamil, Muhammad
dc.contributor.authorKountche, Boubacar Amadou
dc.contributor.authorZarban, Randa Alhassan Yahya
dc.contributor.authorFelemban, Abrar
dc.contributor.authorWang, Jian You
dc.contributor.authorLin, Pei-Yu
dc.contributor.authorHaider, Imran
dc.contributor.authorZurbriggen, Matias
dc.contributor.authorGhanem, Bernard
dc.contributor.authorAl-Babili, Salim
dc.date.accessioned2021-04-19T06:46:14Z
dc.date.available2021-04-19T06:46:14Z
dc.date.issued2021-04-15
dc.date.submitted2020-04-20
dc.identifier.citationJustine Braguy, Merey Ramazanova, Silvio Giancola, Muhammad Jamil, Boubacar A Kountche, Randa Zarban, Abrar Felemban, Jian You Wang, Pei-Yu Lin, Imran Haider, Matias Zurbriggen, Bernard Ghanem, Salim Al-Babili, SeedQuant: a deep learning-based tool for assessing stimulant and inhibitor activity on root parasitic seeds, Plant Physiology, 2021;, kiab173, https://doi.org/10.1093/plphys/kiab173
dc.identifier.issn0032-0889
dc.identifier.pmid33856485
dc.identifier.doi10.1093/plphys/kiab173
dc.identifier.urihttp://hdl.handle.net/10754/668823
dc.description.abstractWitchweeds (Striga spp.) and broomrapes (Orobanchaceae and Phelipanche spp.) are root parasitic plants that infest many crops in warm and temperate zones, causing enormous yield losses and endangering global food security. Seeds of these obligate parasites require rhizospheric, host-released stimulants to germinate, which opens up possibilities for controlling them by applying specific germination inhibitors or synthetic stimulants that induce lethal germination in host's absence. To determine their effect on germination, root exudates or synthetic stimulants/inhibitors are usually applied to parasitic seeds in in vitro bioassays, followed by assessment of germination ratios. Although these protocols are very sensitive, the germination recording process is laborious, representing a challenge for researchers and impeding high-throughput screens. Here, we developed an automatic seed census tool to count and discriminate germinated from non-germinated seeds. We combined deep learning, a powerful data-driven framework that can accelerate the procedure and increase its accuracy, for object detection with computer vision latest development based on the Faster R-CNN algorithm. Our method showed an accuracy of 94% in counting seeds of Striga hermonthica and reduced the required time from ˜5 minutes to 5 seconds per image. Our proposed software, SeedQuant, will be of great help for seed germination bioassays and enable high-throughput screening for germination stimulants/inhibitors. ​SeedQuant is an open-source software that can be further trained to count different types of seeds for research purposes.
dc.description.sponsorshipWe thank Xavier Pita, scientific illustrator at King Abdullah University of Science and Technology (KAUST) for producing Figure 1 and 5, and Raul Masteling (Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, the Netherlands) and Dr. Steven Runo (Department of Biochemistry and Biotechnology, Kenyatta University, Nairobi, Kenya) for sharing disc pictures containing Striga seeds (germinated and nongerminated).
dc.description.sponsorshipThis work was supported by the Bill & Melinda Gates Foundation grant OPP1194472 given to SA and baseline funding from King Abdullah University of Science and Technology given to both SA and B.G.
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/plphys/advance-article/doi/10.1093/plphys/kiab173/6226524
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleSeedQuant: A deep learning-based tool for assessing stimulant and inhibitor activity on root parasitic seeds.
dc.typeArticle
dc.contributor.departmentPlant Science
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentComputer Science
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentVisual Computing Center (VCC)
dc.contributor.departmentBioscience Program
dc.contributor.departmentKing Abdullah University of Science and Technology, Division of Biological and Environmental Science and Engineering, the BioActives Lab, Thuwal, 23955-6900, Saudi Arabia.
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentDesert Agriculture Initiative
dc.identifier.journalPlant physiology
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionInstitute of Synthetic Biology and CEPLAS, University of Düsseldorf, Universitätstrasse 1, Building 26.12.U1.25, Düsseldorf 40225, Germany.
kaust.personBraguy, Justine
kaust.personRamazanova, Merey
kaust.personGiancola, Silvio
kaust.personJamil, Muhammad
kaust.personKountche, Boubacar Amadou
kaust.personZarban, Randa Alhassan Yahya
kaust.personFelemban, Abrar Sami Mahfoz
kaust.personWang, Jian You
kaust.personLin, Pei-Yu
kaust.personHaider, Imran
kaust.personGhanem, Bernard
kaust.personAl-Babili, Salim
dc.date.accepted2021-03-25
dc.relation.issupplementedbygithub:SilvioGiancola/maskrcnn-benchmark
refterms.dateFOA2021-04-19T06:47:02Z
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Software]</i> <br/> Title: SilvioGiancola/maskrcnn-benchmark:. Publication Date: 2020-01-27. github: <a href="https://github.com/SilvioGiancola/maskrcnn-benchmark" >SilvioGiancola/maskrcnn-benchmark</a> Handle: <a href="http://hdl.handle.net/10754/669524" >10754/669524</a></a></li></ul>
kaust.acknowledged.supportUnitscientific illustrator at King Abdullah University of Science and Technology (KAUST)
kaust.acknowledged.supportUnitBaseline funding


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This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.