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2021 Vol.66, Issue 4 Preview Page

Review Aticle

Imagery Acquisition Methods for Root Analysis in Crops under Field Conditions

포장에서 작물의 뿌리분석을 위한 이미지 획득방법

Yoonha Kim1*
김 윤하1*
1Assistant Professor, Department of Applied Bioscience, Kyungpook National University, Daegu 41566
1경북대학교 응용생명과학과 조교수
*Corresponding Author
1 December 2021. pp. 452-458
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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.

Keywords
root architecture
root image
root phenotype
root research
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. 10.1104/pp.114.24351925187526PMC4213080
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. 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. 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. 10.3389/fpls.2020.52016133193467PMC7655541
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. 10.1186/s13007-015-0093-326535051PMC4630929
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. 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.
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. 10.3389/fpls.2021.70423934421953PMC8374737
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. 10.3390/ijms2104151332098434PMC7073213
10
Lobet, G., X. Draye, and C. Périlleux. 2013. An online database for plant image analysis software tools. Plant Methods 9: 1-7. 10.1186/1746-4811-9-3824107223PMC3853381
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. 10.1104/pp.01027111743120PMC133580
12
Noh, T. K. and D. S. Kim. 2018. Weed research using plant image science. Weed Turf. Sci. 7(4) : 285-296.
13
Pang, W., W. T. Crow, J. E. Luc, R. McSorley, R. M. Giblin- Davis, 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. 10.1094/PDIS-01-11-002630731688
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. 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. 10.3390/plants1005088533924781PMC8145683
16
Wachsman, G., E. E. Sparks, P. N. B. 2015. Genes and networks regulating root anatomy and architecture. New Phytol. 208: 26-38. 10.1111/nph.1346925989832
17
Water, A., F. Liebisch, and A. Hund. 2015. Plant phenotyping: from bean weight to image analysis. Plant Method. 10.1186/s13007-015-0056-825767559PMC4357161
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. 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. 10.1093/jxb/erw49428168270PMC5441853
Information
  • Publisher :The Korean Society of Crop Science
  • Publisher(Ko) :한국작물학회
  • Journal Title :The Korean Journal of Crop Science
  • Journal Title(Ko) :한국작물학회지
  • Volume : 66
  • No :4
  • Pages :452-458
  • Received Date : 2021-10-12
  • Revised Date : 2021-10-25
  • Accepted Date : 2021-11-01
  • DOI :https://doi.org/10.7740/kjcs.2021.66.4.452
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