The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Automated plant species identification—Trends and future directions
Open accessAbstract
Current rates of species loss triggered numerous attempts to protect and conserve biodiversity. Species conservation, however, requires species identification skills, a competence obtained through intensive training and experience. Field researchers, land managers, educators, civil servants, and the interested public would greatly benefit from accessible, up-to-date tools automating the process of species identification. Currently, relevant technologies, such as digital cameras, mobile devices, and remote access to databases, are ubiquitously available, accompanied by significant advances in image processing and pattern recognition. The idea of automated species identification is approaching reality. We review the technical status quo on computer vision approaches for plant species identification, highlight the main research challenges to overcome in providing applicable tools, and conclude with a discussion of open and future research thrusts.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Accelerated modern human–induced species losses: Entering the sixth mass extinction
Ceballos, G., Ehrlich, P. R., Barnosky, A. D., García, A., Pringle, R. M., & Palmer, T. M. (2015) · Science Advances · doi:10.1126/sciadv.1400253 · Cited by 4,220 · cited 1×
“With the continuous loss of biodiversity [11], the demand for routine species identification is likely to further increase, while at the same time, the number of experienced experts is limited and declining [12].”
BackgroundAutomated Flower Classification over a Large Number of Classes
Nilsback, M.-E., & Zisserman, A. (2008) · 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing · doi:10.1109/icvgip.2008.47 · Cited by 3,442
How Many Species Are There on Earth and in the Ocean?
Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G. B., & Worm, B. (2011) · PLoS Biology · doi:10.1371/journal.pbio.1001127 · Cited by 3,045 · cited 1×
“Current estimates of flowering plant species (angiosperms) range between 220,000 [4, 5] and 420,000 [6].”
Background
Cited by
Digitization and the Future of Natural History Collections
Hedrick, B. P., Heberling, J. M., Meineke, E. K., Turner, K. G., Grassa, C. J., Park, D. S., Kennedy, J., Clarke, J. A., Cook, J. A., Blackburn, D. C., et al. (2020) · BioScience · doi:10.1093/biosci/biz163 · Cited by 306
Mana, A. A., Allouhi, A., Hamrani, A., Rehman, S., el Jamaoui, I., & Jayachandran, K. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100416 · Cited by 245
Shivaprakash, K. N., Swami, N., Mysorekar, S., Arora, R., Gangadharan, A., Vohra, K., Jadeyegowda, M., & Kiesecker, J. M. (2022) · Sustainability · doi:10.3390/su14127154 · Cited by 194
Metadata
- Authors
- Wäldchen, J., Rzanny, M., Seeland, M., & Mäder, P.
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-04-05
- Volume
- 14
- Issue
- 4
- Pages
- e1005993
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W2795866616
- Cited by
- 340
- References
- 7
- FWCI
- 25.69
- Type
- journal article
- Language
- en
- Open access
- gold
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- Wäldchen, J., Rzanny, M., Seeland, M., & Mäder, P. (2018). Automated plant species identification—Trends and future directions. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1005993
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