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 DepartmentBiological and Environmental Science and Engineering (BESE) Division
Center for Desert Agriculture
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Electrical and Computer Engineering Program
Environmental Science and Engineering Program
King Abdullah University of Science and Technology, Division of Biological and Environmental Science and Engineering, the BioActives Lab, Thuwal, 23955-6900, Saudi Arabia.
VCC Analytics Research Group
Visual Computing Center (VCC)
Online Publication Date2021-04-15
Print Publication Date2021-07-06
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
AcknowledgementsWe 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.