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Image Analysis for Measuring Disease Symptom to Bacterial Soft Rot in Potato

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Abstract

Bacterial soft rot is a devastating disease in potato. However, it is difficult to evaluate disease resistance because there are a number of ways the bacterium can infect tubers, including through lenticels, in bruised tissue, and through wounds. Thus, various screening methods have been developed to evaluate resistance in potato tubers. The methods published to date are limited in their ability to measure symptoms quickly and accurately in a large number of samples. Therefore, we developed a new high throughput phenotyping method to evaluate soft rot disease symptoms the assistance of image analysis software. This method has proven to be very efficient in evaluating disease symptoms.

Resumen

La pudrición blanda por bacterias es una enfermedad devastadora en papa. No obstante, es difícil evaluar la resistencia a la enfermedad porque hay muchas formas en que la bacteria puede infectar a los tubérculos, incluyendo a través de lenticelas, en tejido raspado o heridas. En consecuencia, se han desarrollado varios métodos de evaluación para la resistencia en tubérculos de papa. Los métodos publicados a la fecha están limitados en su capacidad para medir los síntomas rápidamente y con precisión en un número grande de muestras. Entonces, nosotros desarrollamos un método nuevo de alta eficiencia de caracterización fenotípica para evaluar los síntomas de la pudrición blanda mediante la asistencia de un programa de análisis de imágenes. Este método ha probado ser muy eficiente en la evaluación de los síntomas de la enfermedad.

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References

  • Barbedo, J.G. 2013. Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus 2: 660.

    Article  Google Scholar 

  • Barbedo, J.G. 2014. An automatic method to detect and measure leaf disease symptoms using digital image processing. Plant Disease 98: 1709–1716.

    Article  PubMed  Google Scholar 

  • Bock, C.H., G.H. Poole, P.E. Parker, and T.R. Gottwald. 2010. Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Critical Reviews in Plant Sciences 29: 59–107.

    Article  Google Scholar 

  • Chung, Y.S., C. Kim, and S. Jansky. 2017. New source of bacterial soft rot resistance in wild potato (Solanum chacoense) tubers. Genetic Resources and Crop Evolution 64: 1963–1969.

    Article  Google Scholar 

  • Czajkowski, R., M.C. Pérombelon, S. Jafra, E. Lojkowska, M. Potrykus, J.M. Van Der Wolf, and W. Sledz. 2015. Detection, identification and differentiation of Pectobacterium and Dickeya species causing potato blackleg and tuber soft rot: A review. Annals of Applied Biology 166: 18–38.

    Article  CAS  PubMed  Google Scholar 

  • Dutta, A., Gupta, A., and Zissermann, A. 2016. Image Annotator. http://www.robots.ox.ac.uk/~vgg/software/via

  • Fahlgren, N., M.A. Gehan, and I. Baxter. 2015. Lights, camera, action: High throughput plant phenotyping is ready for a close-up. Current Opinion in Plant Biology 24: 93–99.

    Article  PubMed  Google Scholar 

  • Koppel, M. 1993. Methods of assessing potato tubers for resistance to bacterial soft rot. Potato Research 36: 183–188.

    Article  Google Scholar 

  • Kulkarni, N. 2012. Color thresholding method for image segmentation of natural images. International Journal of Image, Graphics and Signal Processing 4: 28–34.

    Article  Google Scholar 

  • Lapwood, D.H., P.J. Read, and J. Spokes. 1984. Methods for assessing the susceptibility of potato tubers of different cultivars to rotting by Erwinia carotovora subspecies atroseptica and carotovora. Plant Pathology 33: 13–20.

    Article  Google Scholar 

  • Łojkowska, E., and A. Kelman. 1994. Comparison of the effectiveness of different methods of screening for bacterial soft rot resistance of potato tubers. American Potato Journal 71: 99–113.

    Article  Google Scholar 

  • Ma, B., M.E. Hibbing, H.S. Kim, R.M. Reedy, I. Yedidia, J. Breuer, J. Breuer, J.D. Glasner, N.T. Perna, A. Kelman, and A.O. Charkowski. 2007. Host range and molecular phylogenies of the soft rot enterobacterial genera Pectobacterium and Dickeya. Phytopathology 97: 1150–1163.

    Article  PubMed  Google Scholar 

  • McGuire, R.G., and A. Kelman. 1984. Reduced severity of Erwinia soft rot in potato tubers with increased calcium content. Phytopathology 74: 1250–1256.

    Article  CAS  Google Scholar 

  • O’neal, M.E., D.A. Landis, and R. Isaacs. 2002. An inexpensive, accurate method for measuring leaf area and defoliation through digital image analysis. Journal of Economic Entomology 95: 1190–1194.

    Article  PubMed  Google Scholar 

  • Olaf, R., F. Philipp, and B. Thomas. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention: 234–241.

  • Patterson, H.D., and R. Thompson. 1971. Recovery of inter-block information when block sizes are unequal. Biometrika 58: 545–554.

    Article  Google Scholar 

  • Pérombelon, M.C., and A. Kelman. 1980. Ecology of the soft rot erwinias. Annual Review of Phytopathology 18: 361–387.

    Article  Google Scholar 

  • Pinheiro, J.C., and D.M. Bates. 2000. Mixed-effects models in S and S-PLUS. New York: Springer Verlag.

    Book  Google Scholar 

  • Priou, S., K. A.I. Ani, and B. Jouan. 1992. Comparison of the effectiveness of two methods of screening potato soft rot induced by Erwinia carotovora subsp, atroseptica (Van Hall 1902). Proceedings of the Joint Conference of the EA PR Breeding & Varietal Assessment Section and the EUCARPIA Potato Section, Landerneau, France, pp. 139–140.

  • Wijekoon, C.P., P.H. Goodwin, and T. Hsiang. 2008. Quantifying fungal infection of plant leaves by digital image analysis using Scion image software. Journal of Microbiological Methods 74: 94–101.

    Article  CAS  PubMed  Google Scholar 

  • Venables, W.N., Smith, D.M., and Team, R.C. 2018. An introduction to R-Notes on R: A programming environment for data analysis and graphics

  • Yap, M.N., J.D. Barak, and A.O. Charkowski. 2004. Genomic diversity of Erwinia carotovora subsp. carotovora and its correlation with virulence. Applied and Environmental Microbiology 70: 3013–3023.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We are grateful to Sustainable Agriculture Research Institute (SARI) in Jeju National University for providing the experimental facilities.

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Author notes

  1. Unseok Lee, Renato Rodrigues Silva and Changsoo Kim contributed equally to this work.

Authors and Affiliations

  1. Convergence Research Center for Smart Farm Solution, Korea Institute of Science and Technology, Gangneung, Gangwon-do, 25451, Republic of Korea

    Unseok Lee & Hyoungseok Kim

  2. Institute of Mathematics and Statistics, Federal University of Goiás, Goiânia, Brazil

    Renato Rodrigues Silva

  3. Department of Crop Science, Chungnam National University, Daejeon, 34134, Republic of Korea

    Changsoo Kim

  4. Incheon Airport Office, Animal and Plant Quarantine Agency, Ministry of Agriculture, Food and Rural Affairs, Sejong, City, Republic of Korea

    Seong Heo

  5. Department of Undeclared Majors, Kyungil University, Gyeongsan-si, 38428, Republic of Korea

    In Sung Park

  6. Department of Biosystems and Biotechnology, Korea University, Seoul, 02841, Republic of Korea

    Wook Kim

  7. U.S. Department of Agriculture–Agricultural Research Service and Department of Horticulture, University of Wisconsin, 1575 Linden Drive, Madison, 53706, USA

    Shelley Jansky

  8. Faculty of Bioscience and Industry, College of Applied Life Science, SARI, Jeju National University, Jeju, 63243, Republic of Korea

    Yong Suk Chung

Authors
  1. Unseok Lee
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  2. Renato Rodrigues Silva
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  3. Changsoo Kim
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  4. Hyoungseok Kim
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  5. Seong Heo
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  6. In Sung Park
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  7. Wook Kim
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  8. Shelley Jansky
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  9. Yong Suk Chung
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Corresponding authors

Correspondence to Shelley Jansky or Yong Suk Chung.

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Lee, U., Silva, R.R., Kim, C. et al. Image Analysis for Measuring Disease Symptom to Bacterial Soft Rot in Potato. Am. J. Potato Res. 96, 303–313 (2019). https://doi.org/10.1007/s12230-019-09717-8

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  • DOI: https://doi.org/10.1007/s12230-019-09717-8

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