The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Machine Learning for High-Throughput Stress Phenotyping in Plants
Open accessAbstract
Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.
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Cites
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Cited by
Plant disease identification using explainable 3D deep learning on hyperspectral images
Nagasubramanian, K., Jones, S., Singh, A. K., Sarkar, S., Singh, A., & Ganapathysubramanian, B. (2019) · Plant Methods · doi:10.1186/s13007-019-0479-8 · Cited by 394
Self-citationDeep 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
PlantCV v2: Image analysis software for high-throughput plant phenotyping
Gehan, M. A., Fahlgren, N., Abbasi, A., Berry, J. C., Callen, S. T., Chavez, L., Doust, A. N., Feldman, M. J., Gilbert, K. B., Hodge, J. G., et al. (2017) · PeerJ · doi:10.7717/peerj.4088 · Cited by 365
Enhancing genetic gain in the era of molecular breeding
Xu, Y., Li, P., Zou, C., Lu, Y., Xie, C., Zhang, X., Prasanna, B. M., & Olsen, M. S. (2017) · Journal of Experimental Botany · doi:10.1093/jxb/erx135 · Cited by 336
Ear density estimation from high resolution RGB imagery using deep learning technique
Madec, S., Jin, X., Lu, H., De Solan, B., Liu, S., Duyme, F., Heritier, E., & Baret, F. (2019) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2018.10.013 · Cited by 312
Metadata
- Authors
- Singh, A., Ganapathysubramanian, B., Singh, A. K., & Sarkar, S.
- Venue
- Trends in Plant Science
- Publisher
- Elsevier BV
- Published
- 2016-02
- Volume
- 21
- Issue
- 2
- Pages
- 110-124
- ISSN
- 1360-1385 (ISSN-L)
- OpenAlex
- W2185489349
- Cited by
- 1,109
- References
- 1
- FWCI
- 4.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Singh, A., Ganapathysubramanian, B., Singh, A. K., & Sarkar, S. (2016). Machine Learning for High-Throughput Stress Phenotyping in Plants. Trends in Plant Science. https://doi.org/10.1016/j.tplants.2015.10.015
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