The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Plant Phenotyping: Past, Present, and Future
Open accessAbstract
A plant develops the dynamic phenotypes from the interaction of the plant with the environment. Understanding these processes that span plant's lifetime in a permanently changing environment is essential for the advancement of basic plant science and its translation into application including breeding and crop management. The plant research community was thus confronted with the need to accurately measure diverse traits of an increasingly large number of plants to help plants to adapt to resource-limiting environment and low-input agriculture. In this overview, we outline the development of plant phenotyping as a multidisciplinary field. We sketch the technological advancement that laid the foundation for the development of phenotyping centers and evaluate the upcoming challenges for further advancement of plant phenotyping specifically with respect to standardization of data acquisition and reusability. Finally, we describe the development of the plant phenotyping community as an essential step to integrate the community and effectively use the emerging synergies.
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The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423
Zohary, D., Hopf, M., & Weiss, E. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:osobl/9780199549061.001.0001 · Cited by 2,987
Phenomics – technologies to relieve the phenotyping bottleneck
Furbank, R. T., & Tester, M. (2011) · Trends in Plant Science · doi:10.1016/j.tplants.2011.09.005 · Cited by 1,675
Phenotypic plasticity for plant development, function and life history
Sultan, S. E. (2000) · Trends in Plant Science · doi:10.1016/s1360-1385(00)01797-0 · Cited by 1,581
Houle, D., Govindaraju, D. R., & Omholt, S. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2897 · Cited by 1,279
Future Scenarios for Plant Phenotyping
Fiorani, F., & Schurr, U. (2013) · Annual Review of Plant Biology · doi:10.1146/annurev-arplant-050312-120137 · Cited by 995
Self-citationReview of 20 years of range sensor development
Blais, F. (2004) · Journal of Electronic Imaging · doi:10.1117/1.1631921 · Cited by 907
Research priorities for harnessing plant microbiomes in sustainable agriculture
Busby, P. E., Soman, C., Wagner, M. R., Friesen, M. L., Kremer, J., Bennett, A., Morsy, M., Eisen, J. A., Leach, J. E., & Dangl, J. L. (2017) · PLOS Biology · doi:10.1371/journal.pbio.2001793 · Cited by 840
Big Data and cloud computing: innovation opportunities and challenges
Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017) · International Journal of Digital Earth · doi:10.1080/17538947.2016.1239771 · Cited by 838
Smart farming is key to developing sustainable agriculture
Walter, A., Finger, R., Huber, R., & Buchmann, N. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707462114 · Cited by 789
Cited by
Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review
Jiang, Y., & Li, C. (2020) · Plant Phenomics · doi:10.34133/2020/4152816 · Cited by 380
Metadata
- Authors
- Pieruschka, R., & Schurr, U.
- Venue
- Plant Phenomics
- Publisher
- Elsevier BV
- Published
- 2019
- Volume
- 2019
- Pages
- 7507131
- ISSN
- 2097-0374 (ISSN-L)
- 2643-6515
- OpenAlex
- W2925510773
- Cited by
- 341
- References
- 28
- FWCI
- 28.00
- Type
- journal article
- Language
- en
- Open access
- gold
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