New method for detecting Collectorichum species found in Korea using image analysis

Authors

  • JeongHo Baek Gene Engineering Division, National Institute of Agricultural Sciences, RDA, Jeonju, 54874, Republic of Korea
  • Nyunhee Kim Gene Engineering Division, National Institute of Agricultural Sciences, RDA, Jeonju, 54874, Republic of Korea
  • JaeYoung Kim Gene Engineering Division, National Institute of Agricultural Sciences, RDA, Jeonju, 54874, Republic of Korea
  • Younguk Kim Gene Engineering Division, National Institute of Agricultural Sciences, RDA, Jeonju, 54874, Republic of Korea
  • Chaewon Lee Gene Engineering Division, National Institute of Agricultural Sciences, RDA, Jeonju, 54874, Republic of Korea
  • Song Lim Kim Gene Engineering Division, National Institute of Agricultural Sciences, RDA, Jeonju, 54874, Republic of Korea
  • Hyeonso Ji Gene Engineering Division, National Institute of Agricultural Sciences, RDA, Jeonju, 54874, Republic of Korea
  • Sang Ryeol Park Gene Engineering Division, National Institute of Agricultural Sciences, RDA, Jeonju, 54874, Republic of Korea
  • Inchan Choi Division of Smart Farm Development, National Institute of Agricultural Sciences, RDA, Jeonju, 54875, Republic of Korea
  • Kyung-Hwan Kim Gene Engineering Division, National Institute of Agricultural Sciences, RDA, Jeonju, 54874, Republic of Korea

DOI:

https://doi.org/10.25081/jp.2022.v14.7337

Keywords:

Chili pepper disease, Color analysis, Pathogen phenotyping, RGB imaging

Abstract

Colletotrichum acutatum spp. infects various economical crops worldwide and causes massive loss on their yields. Among those, Capsicum spp., which known as chili pepper, is on a critical issue by those pathogens. Due to the lack of their genetic markers in Korea, the unidentifiable various species of C. acutatum obstructs the mechanism studies of these pathogens and the selection of disease resistant breed lines. Therefore, we screened RGB images of the colonization progresses of pathogens to identify the species of Ca40042, K1, NN, AS2, and SW1 by time and temperature. Cultivated pathogens such as Ca40042, K1, and SW1 were detectable on quantified shape and color data of images from specific temperature conditions, while other pathogens were difficult to recognize. Although several limitations exist in identification results of current experiment, but also, we can expect this method can suggest the possibility to replace the genetic marker methods which is now unavailable in Korea.

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References

Barbedo, J. G. A. (2013). Digital image processing techniques for detecting, quantifying and classifying plant diseases. Springerplus, 2(1), 1-12. https://doi.org/10.1186/2193-1801-2-660

Dörge, T., Carstensen, J. M., & Frisvad, J. C. (2000). Direct identification of pure Penicillium species using image analysis. Journal of Microbiological Methods, 41(2), 121-133. https://doi.org/10.1016/S0167-7012(00)00142-1

Kang, B. K., Min, J. Y., Kim, Y. S., Park, S. W., Bach, N. V., & Kim, H. T. (2005). Semi-selective medium for monitoring Colletotrichum acutatum causing pepper anthracnose in the field. Research in Plant Disease, 11(1), 21-27. https://doi.org/10.5423/RPD.2005.11.1.021

Liao, C. Y., Chen, M. Y., Chen, Y. K., Kuo, K. C., Chung, K. R., & Lee, M. H. (2012). Formation of highly branched hyphae by Colletotrichum acutatum within the fruit cuticles of Capsicum spp. Plant Pathology, 61(2), 262-270. https://doi.org/10.1111/j.1365-3059.2011.02523.x

Peres, N. A., Timmer, L. W., Adaskaveg, J. E., & Correll, J. C. (2005). Lifestyles of Colletotrichum acutatum. Plant disease, 89(8), 784-796. https://doi.org/10.1094/PD-89-0784

Perfect, S. E., Hughes, H. B., O' Connell, R. J., & Green, J. R. (1999). Colletotrichum: a model genus for studies on pathology and fungal–plant interactions. Fungal genetics and Biology, 27(2-3), 186-198. https://doi.org/10.1006/fgbi.1999.1143

Pietrowski, A., Flessa, F., & Rambold, G. (2012). Towards an efficient phenotypic classification of fungal cultures from environmental samples using digital imagery. Mycological progress, 11(2), 383-393. https://doi.org/10.1007/s11557-011-0753-2

Puchkov, E. O. (2010). Computer image analysis of microbial colonies. Microbiology, 79(2), 141-146. https://doi.org/10.1134/S0026261710020025

Pujari, J. D., Yakkundimath, R., & Byadgi, A. S. (2015). Image processing based detection of fungal diseases in plants. Procedia Computer Science, 46, 1802-1808. https://doi.org/10.1016/j.procs.2015.02.137

Singh, M. K., Chetia, S., & Singh, M. (2017). Detection and classification of plant leaf diseases in image processing using MATLAB. International journal of life sciences Research, 5(4), 120-124.

Than, P. P., Jeewon, R., Hyde, K. D., Pongsupasamit, S., Mongkolporn, O., & Taylor, P. W. J. (2008). Characterization and pathogenicity of Colletotrichum species associated with anthracnose on chilli (Capsicum spp.) in Thailand. Plant Pathology, 57(3), 562-572. https://doi.org/10.1111/j.1365-3059.2007.01782.x

Tozze Jr, H. J., Massola Jr, N. M., Camara, M. P. S., Gioria, R., Suzuki, O., Brunelli, K. R., Braga, R. S., & Kobori, R. F. (2009). First report of Colletotrichum boninense causing anthracnose on pepper in Brazil. Plant Disease, 93(1), 106-106. https://doi.org/10.1094/PDIS-93-1-0106A

Published

29-01-2022

How to Cite

Baek, J., Kim, N., Kim, J., Kim, Y., Lee, C., Kim, S. L., Ji, H., Park, S. R., Choi, I., & Kim, K.-H. (2022). New method for detecting Collectorichum species found in Korea using image analysis. Journal of Phytology, 14, 1–7. https://doi.org/10.25081/jp.2022.v14.7337

Issue

Volume 14, 2022

Section

Articles

Copyright (c) 2022 JeongHo Baek, Nyunhee Kim, JaeYoung Kim, Younguk Kim, Chaewon Lee, Song Lim Kim, Hyeonso Ji, Sang Ryeol Park, Inchan Choi, Kyung-Hwan Kim

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.