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DiSCount: computer vision for automated quantification of Striga seed germination
BACKGROUND: Plant parasitic weeds belonging to the genus Striga are a major threat for food production in Sub-Saharan Africa and Southeast Asia. The parasite’s life cycle starts with the induction of seed germination by host plant-derived signals, followed by parasite attachment, infection, outgrowt...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7195706/ https://www.ncbi.nlm.nih.gov/pubmed/32377220 http://dx.doi.org/10.1186/s13007-020-00602-8 |
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author | Masteling, Raul Voorhoeve, Lodewijk IJsselmuiden, Joris Dini-Andreote, Francisco de Boer, Wietse Raaijmakers, Jos M. |
author_facet | Masteling, Raul Voorhoeve, Lodewijk IJsselmuiden, Joris Dini-Andreote, Francisco de Boer, Wietse Raaijmakers, Jos M. |
author_sort | Masteling, Raul |
collection | PubMed |
description | BACKGROUND: Plant parasitic weeds belonging to the genus Striga are a major threat for food production in Sub-Saharan Africa and Southeast Asia. The parasite’s life cycle starts with the induction of seed germination by host plant-derived signals, followed by parasite attachment, infection, outgrowth, flowering, reproduction, seed set and dispersal. Given the small seed size of the parasite (< 200 μm), quantification of the impact of new control measures that interfere with seed germination relies on manual, labour-intensive counting of seed batches under the microscope. Hence, there is a need for high-throughput assays that allow for large-scale screening of compounds or microorganisms that adversely affect Striga seed germination. RESULTS: Here, we introduce DiSCount (Digital Striga Counter): a computer vision tool for automated quantification of total and germinated Striga seed numbers in standard glass fibre filter assays. We developed the software using a machine learning approach trained with a dataset of 98 manually annotated images. Then, we validated and tested the model against a total dataset of 188 manually counted images. The results showed that DiSCount has an average error of 3.38 percentage points per image compared to the manually counted dataset. Most importantly, DiSCount achieves a 100 to 3000-fold speed increase in image analysis when compared to manual analysis, with an inference time of approximately 3 s per image on a single CPU and 0.1 s on a GPU. CONCLUSIONS: DiSCount is accurate and efficient in quantifying total and germinated Striga seeds in a standardized germination assay. This automated computer vision tool enables for high-throughput, large-scale screening of chemical compound libraries and biological control agents of this devastating parasitic weed. The complete software and manual are hosted at https://gitlab.com/lodewijk-track32/discount_paper and the archived version is available at Zenodo with the DOI 10.5281/zenodo.3627138. The dataset used for testing is available at Zenodo with the DOI 10.5281/zenodo.3403956. |
format | Online Article Text |
id | pubmed-7195706 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-71957062020-05-06 DiSCount: computer vision for automated quantification of Striga seed germination Masteling, Raul Voorhoeve, Lodewijk IJsselmuiden, Joris Dini-Andreote, Francisco de Boer, Wietse Raaijmakers, Jos M. Plant Methods Software BACKGROUND: Plant parasitic weeds belonging to the genus Striga are a major threat for food production in Sub-Saharan Africa and Southeast Asia. The parasite’s life cycle starts with the induction of seed germination by host plant-derived signals, followed by parasite attachment, infection, outgrowth, flowering, reproduction, seed set and dispersal. Given the small seed size of the parasite (< 200 μm), quantification of the impact of new control measures that interfere with seed germination relies on manual, labour-intensive counting of seed batches under the microscope. Hence, there is a need for high-throughput assays that allow for large-scale screening of compounds or microorganisms that adversely affect Striga seed germination. RESULTS: Here, we introduce DiSCount (Digital Striga Counter): a computer vision tool for automated quantification of total and germinated Striga seed numbers in standard glass fibre filter assays. We developed the software using a machine learning approach trained with a dataset of 98 manually annotated images. Then, we validated and tested the model against a total dataset of 188 manually counted images. The results showed that DiSCount has an average error of 3.38 percentage points per image compared to the manually counted dataset. Most importantly, DiSCount achieves a 100 to 3000-fold speed increase in image analysis when compared to manual analysis, with an inference time of approximately 3 s per image on a single CPU and 0.1 s on a GPU. CONCLUSIONS: DiSCount is accurate and efficient in quantifying total and germinated Striga seeds in a standardized germination assay. This automated computer vision tool enables for high-throughput, large-scale screening of chemical compound libraries and biological control agents of this devastating parasitic weed. The complete software and manual are hosted at https://gitlab.com/lodewijk-track32/discount_paper and the archived version is available at Zenodo with the DOI 10.5281/zenodo.3627138. The dataset used for testing is available at Zenodo with the DOI 10.5281/zenodo.3403956. BioMed Central 2020-05-01 /pmc/articles/PMC7195706/ /pubmed/32377220 http://dx.doi.org/10.1186/s13007-020-00602-8 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Masteling, Raul Voorhoeve, Lodewijk IJsselmuiden, Joris Dini-Andreote, Francisco de Boer, Wietse Raaijmakers, Jos M. DiSCount: computer vision for automated quantification of Striga seed germination |
title | DiSCount: computer vision for automated quantification of Striga seed germination |
title_full | DiSCount: computer vision for automated quantification of Striga seed germination |
title_fullStr | DiSCount: computer vision for automated quantification of Striga seed germination |
title_full_unstemmed | DiSCount: computer vision for automated quantification of Striga seed germination |
title_short | DiSCount: computer vision for automated quantification of Striga seed germination |
title_sort | discount: computer vision for automated quantification of striga seed germination |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7195706/ https://www.ncbi.nlm.nih.gov/pubmed/32377220 http://dx.doi.org/10.1186/s13007-020-00602-8 |
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