The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Uncovering the hidden half of plants using new advances in root phenotyping
Open accessAbstract
Major increases in crop yield are required to keep pace with population growth and climate change. Improvements to the architecture of crop roots promise to deliver increases in water and nutrient use efficiency but profiling the root phenome (i.e. its structure and function) represents a major bottleneck. We describe how advances in imaging and sensor technologies are making root phenomic studies possible. However, methodological advances in acquisition, handling and processing of the resulting 'big-data' is becoming increasingly important. Advances in automated image analysis approaches such as Deep Learning promise to transform the root phenotyping landscape. Collectively, these innovations are helping drive the selection of the next-generation of crops to deliver real world impact for ongoing global food security efforts.
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Citations
Cites
Roots of the Second Green Revolution
Lynch, J. P. (2007) · Australian Journal of Botany · doi:10.1071/bt06118 · Cited by 1,282
Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field
Trachsel, S., Kaeppler, S. M., Brown, K. M., & Lynch, J. P. (2011) · Plant and Soil · doi:10.1007/s11104-010-0623-8 · Cited by 731
Plant Phenomics, From Sensors to Knowledge
Tardieu, F., Cabrera-Bosquet, L., Pridmore, T., & Bennett, M. (2017) · Current Biology · doi:10.1016/j.cub.2017.05.055 · Cited by 614
Self-citationThree-Dimensional Root Phenotyping with a Novel Imaging and Software Platform
Clark, R. T., MacCurdy, R. B., Jung, J. K., Shaff, J. E., McCouch, S. R., Aneshansley, D. J., & Kochian, L. V. (2011) · Plant Physiology · doi:10.1104/pp.110.169102 · Cited by 471
Developing X-ray Computed Tomography to non-invasively image 3-D root systems architecture in soil
Mooney, S. J., Pridmore, T. P., Helliwell, J., & Bennett, M. J. (2012) · Plant and Soil · doi:10.1007/s11104-011-1039-9 · Cited by 454
Self-citationPlant roots use a patterning mechanism to position lateral root branches toward available water
Bao, Y., Aggarwal, P., Robbins, N. E., Sturrock, C. J., Thompson, M. C., Tan, H. Q., Tham, C., Duan, L., Rodriguez, P. L., Vernoux, T., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1400966111 · Cited by 415
Self-citationHolman, F., Riche, A., Michalski, A., Castle, M., Wooster, M., & Hawkesford, M. (2016) · Remote Sensing · doi:10.3390/rs8121031 · Cited by 398
Deep machine learning provides state-of-the-art performance in image-based plant phenotyping
Pound, M. P., Atkinson, J. A., Townsend, A. J., Wilson, M. H., Griffiths, M., Jackson, A. S., Bulat, A., Tzimiropoulos, G., Wells, D. M., Murchie, E. H., et al. (2017) · GigaScience · doi:10.1093/gigascience/gix083 · Cited by 389
Self-citationGiA Roots: software for the high throughput analysis of plant root system architecture
Galkovskyi, T., Mileyko, Y., Bucksch, A., Moore, B., Symonova, O., Price, C. A., Topp, C. N., Iyer-Pascuzzi, A. S., Zurek, P. R., Fang, S., et al. (2012) · BMC Plant Biology · doi:10.1186/1471-2229-12-116 · Cited by 387
Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring
Virlet, N., Sabermanesh, K., Sadeghi-Tehran, P., & Hawkesford, M. J. (2016) · Functional Plant Biology · doi:10.1071/fp16163 · Cited by 382
Cited by
Yang, W., Feng, H., Zhang, X., Zhang, J., Doonan, J. H., Batchelor, W. D., Xiong, L., & Yan, J. (2020) · Molecular Plant · doi:10.1016/j.molp.2020.01.008 · Cited by 961
Freschet, G. T., Pagès, L., Iversen, C. M., Comas, L. H., Rewald, B., Roumet, C., Klimešová, J., Zadworny, M., Poorter, H., Postma, J. A., et al. (2021) · New Phytologist · doi:10.1111/nph.17572 · Cited by 668
Xu, Y., Zhang, X., Li, H., Zheng, H., Zhang, J., Olsen, M. S., Varshney, R. K., Prasanna, B. M., & Qian, Q. (2022) · Molecular Plant · doi:10.1016/j.molp.2022.09.001 · Cited by 363
Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence
Harfouche, A. L., Jacobson, D. A., Kainer, D., Romero, J. C., Harfouche, A. H., Scarascia Mugnozza, G., Moshelion, M., Tuskan, G. A., Keurentjes, J. J. B., & Altman, A. (2019) · Trends in Biotechnology · doi:10.1016/j.tibtech.2019.05.007 · Cited by 292
Review: application of artificial intelligence in phenomics
Nabwire, S., Suh, H.-K., Kim, M. S., Baek, I., & Cho, B.-K. (2021) · Sensors · doi:10.3390/s21134363 · Cited by 91
Metadata
- Authors
- Atkinson, J. A., Pound, M. P., Bennett, M. J., & Wells, D. M.
- Venue
- Current Opinion in Biotechnology
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 55
- Pages
- 1-8
- ISSN
- 0958-1669 (ISSN-L)
- OpenAlex
- W2883418202
- Cited by
- 406
- References
- 36
- FWCI
- 5.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
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