SeedQuant: A deep learning-based tool for assessing stimulant and inhibitor activity on root parasitic seeds.
Kountche, Boubacar Amadou
Zarban, Randa Alhassan Yahya
Wang, Jian You
KAUST DepartmentPlant Science
Biological and Environmental Sciences and Engineering (BESE) Division
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Visual Computing Center (VCC)
King Abdullah University of Science and Technology, Division of Biological and Environmental Science and Engineering, the BioActives Lab, Thuwal, 23955-6900, Saudi Arabia.
Electrical Engineering Program
Desert Agriculture Initiative
Permanent link to this recordhttp://hdl.handle.net/10754/668823
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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.
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
SponsorsWe 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).
This 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.
PublisherOxford University Press (OUP)
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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.
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