KOREAN JOURNAL OF CROP SCIENCE (한국작물학회지)

The Korean Society of Crop Science (한국작물학회)
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Imagery Acquisition Methods for Root Analysis in Crops under Field Conditions

포장에서 작물의 뿌리분석을 위한 이미지 획득방법

  • Kim, Yoonha (Department of Applied Bioscience, Kyungpook National University)
  • 김윤하 (경북대학교 응용생명과학과)
  • Received : 2021.10.12
  • Accepted : 2021.11.01
  • Published : 2021.12.01
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Abstract

Roots are the most important organs in plants that absorb nutrients and moisture from the soil. However, owing to difficulties in root data collection, root research is still poorly conducted as compared to shoot research. Recent advancements in crop phenotyping, through advanced imagery data, are rapidly increasing, and artificial intelligence has been applied in various crop root research. Depending on the purpose, different root analysis methods have been developed that measure roots directly in soil or after separation from the soil. Each method has its advantages and disadvantages; therefore, it can be used in accordance with the research interest. Therefore, this review introduces root analysis methods that use imagery systems to help domestic researchers precisely study plant roots or root architecture.

뿌리는 식물에서 양분과 수분을 흡수하는 가장 중요한 기관임에도 불구하고 분석방법에 어려움으로 인해 지상부에 비해 연구가 상대적으로 미비한 실정이다. 최근 이미지를 기반으로 작물의 표현형을 분석하는 기술이 급격하게 발달하고 있으며, 뿌리 연구에 있어서도 이미지를 다양하게 활용하고 있다. 뿌리분석은 목적에 따라 토양에서 분리 후 측정하는 방법과 토양에서 바로 측정하는 방식이 있으며, 각각의 방식들마다 장점과 단점이 있으므로 연구자의 상황에 맞게 활용할 수 있다. 이런 이유에서 본 리뷰에서는 이미지를 활용한 뿌리분석 방법들에 대해 소개하여 국내 연구자들의 뿌리 연구에 이용되기를 바란다.

Keywords

Acknowledgement

본 논문은 농촌진흥청 공동연구사업(과제번호:PJ01567802)의 지원에 의해 이루어진 것임.

References

  1. Bucksch, A., J. Burridge, L. M. York, A. Das, E. Nord, J. S. Weitz, and J. P. Lynch. 2014. Image-based high-throughput field phenotyping of crop roots. Plant Physiol. 166(2) : 470-489. https://doi.org/10.1104/pp.114.243519
  2. Busch, J., I. A. Mendelssohn, B. Lorenzen, H. Brix, and S. Miao. 2006. A rhizotron to study root growth under flooded conditions tested with two wetland Cyperaceae. Flora. 201(6) : 429-439. https://doi.org/10.1016/j.flora.2005.08.007
  3. Cai, G. J. Vanderborght, A. Klotzsche, J. Kruk, J. Neumann, N. Hermes, H. Vereecken. 2016. Vadose Zone J. 15(9) : vzj2016.05.0043. https://doi.org/10.2136/vzj2016.05.0043
  4. Chung, Y. S., U. Lee, S. Heo, R. R. Silva, C. I. Na, and Y. Kim. 2020. Image-based machine learning characterizes root nodule in soybean exposed to silicon. Front. Plant Sci. 11 : 520161. https://doi.org/10.3389/fpls.2020.520161
  5. Das, A., H. Schneider, J. Burridge, A. K. M. Ascanio, T. Wojciechowski, C. N. Topp, J. P. Lynch, J. S. Weitz, and A. Bucksch. 2015. Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics. Plant Methods. 11(1) : 1-12. https://doi.org/10.1186/s13007-015-0043-0
  6. Iversen, C. M., M. T. Murphy, M. F. Allen, J. Childs, D. M. Eissenstat, E. A. Lilleskov, T. M. Sarjala, V. L. Sloan, and P. F. Sullivan. 2012. Advancing the use of minirhizotrons in wetlands. Plant Soil. 352(1) : 23-39. https://doi.org/10.1007/s11104-011-0953-1
  7. Kim, D. W., Y. Kim, K. H. Kim, H. J. Kim, and Y. S. Chung. 2019. Case study: Cost-effective weed patch detection by multi-spectral camera mounted on unmanned aerial vehicle in the buckwheat field. Korean J. Crop Sci. 64(2) : 159-164. https://doi.org/10.7740/KJCS.2019.64.2.159
  8. Kim, K. S., S. H. Kim, J. Kim, P. Tripathi, J. D. Lee, Y. S. Chung, and Y. Kim. 2021. A large root phenome dataset wide-opened the potential for underground breeding in soybean. Front. Plant Sci. 12 : 704239. https://doi.org/10.3389/fpls.2021.704239
  9. Kim, Y., Y. S. Chung, E. Lee, P. Tripathi, S. Heo, and K. H. Kim. 2020. Root response to drought stress in rice (Oryza sativa L.). Int. J. Mol. Sci. 21 : 1513. https://doi.org/10.3390/ijms21041513
  10. Lobet, G., X. Draye, and C. Perilleux. 2013. An online database for plant image analysis software tools. Plant Methods 9: 1-7. https://doi.org/10.1186/1746-4811-9-1
  11. Ma, J. F., S. Goto, K. Tamai, and M. Ichii, 2001. Role of root hairs and lateral roots in silicon uptake by rice. Plant Physiol. 127(4) : 1773-1780. https://doi.org/10.1104/pp.010271
  12. Noh, T. K. and D. S. Kim. 2018. Weed research using plant image science. Weed Turf. Sci. 7(4) : 285-296. https://doi.org/10.5660/WTS.2018.7.4.285
  13. Pang, W., W. T. Crow, J. E. Luc, R. McSorley, R. M. GiblinDavis, K. E. Kenworthy, and J. K. Kruse. 2011. Comparison of water displacement and WinRHIZO software for plant root parameter assessment. Plant Dis. 95(10) : 1308-1310. https://doi.org/10.1094/pdis-01-11-0026
  14. Trachsel, S., S. M. Kaeppler, K. M. Brown, and J. P. Lynch, 2010. Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field. Plant Soil. 341 : 75-87. https://doi.org/10.1007/s11104-010-0623-8
  15. Tripathi, P., S. Subedi, A. L. Khan. Y. S. Chung, and Y. Kim. 2021. Silicon effects on the root system of diverse crop species using root phenotyping technology. Plants. 10 : 885. https://doi.org/10.3390/plants10050885
  16. Wachsman, G., E. E. Sparks, P. N. B. 2015. Genes and networks regulating root anatomy and architecture. New Phytol. 208: 26-38. https://doi.org/10.1111/nph.13469
  17. Water, A., F. Liebisch, and A. Hund. 2015. Plant phenotyping: from bean weight to image analysis. Plant Method.
  18. Yamaguchi, J. 2002. Measurement of root diameter in field-grown crop under a miscroscope without washing. Soil Sci. Plant Nut. 48(4) : 625-629. https://doi.org/10.1080/00380768.2002.10409248
  19. Zhao, J., G. Bodner, B. Rewald, D. Leitner, K. A. Nagel, and A. Nakhforoosh, 2017. Root architecture simulation improves the inference from seedling root phenotyping towards mature root systems. J. Exp. Bot. 68 : 965-982. https://doi.org/10.1093/jxb/erw494