1 |
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.
DOI
|
2 |
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.
DOI
|
3 |
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.
DOI
|
4 |
Lobet, G., X. Draye, and C. Perilleux. 2013. An online database for plant image analysis software tools. Plant Methods 9: 1-7.
DOI
|
5 |
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.
DOI
|
6 |
Wachsman, G., E. E. Sparks, P. N. B. 2015. Genes and networks regulating root anatomy and architecture. New Phytol. 208: 26-38.
DOI
|
7 |
Water, A., F. Liebisch, and A. Hund. 2015. Plant phenotyping: from bean weight to image analysis. Plant Method.
|
8 |
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.
DOI
|
9 |
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.
DOI
|
10 |
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.
DOI
|
11 |
Yamaguchi, J. 2002. Measurement of root diameter in field-grown crop under a miscroscope without washing. Soil Sci. Plant Nut. 48(4) : 625-629.
DOI
|
12 |
Cai, G. J. Vanderborght, A. Klotzsche, J. Kruk, J. Neumann, N. Hermes, H. Vereecken. 2016. Vadose Zone J. 15(9) : vzj2016.05.0043.
DOI
|
13 |
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.
DOI
|
14 |
Noh, T. K. and D. S. Kim. 2018. Weed research using plant image science. Weed Turf. Sci. 7(4) : 285-296.
DOI
|
15 |
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.
DOI
|
16 |
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.
DOI
|
17 |
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.
DOI
|
18 |
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.
DOI
|
19 |
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.
DOI
|