The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks
Open accessAbstract
Plant phenomics has received increasing interest in recent years in an attempt to bridge the genotype-to-phenotype knowledge gap. There is a need for expanded high-throughput phenotyping capabilities to keep up with an increasing amount of data from high-dimensional imaging sensors and the desire to measure more complex phenotypic traits (Knecht et al., 2016). In this paper, we introduce an open-source deep learning tool called Deep Plant Phenomics. This tool provides pre-trained neural networks for several common plant phenotyping tasks, as well as an easy platform that can be used by plant scientists to train models for their own phenotyping applications. We report performance results on three plant phenotyping benchmarks from the literature, including state of the art performance on leaf counting, as well as the first published results for the mutant classification and age regression tasks for Arabidopsis thaliana.
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Cites
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Cited by
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
High-throughput phenotyping: Breaking through the bottleneck in future crop breeding
Song, P., Wang, J., Guo, X., Yang, W., & Zhao, C. (2021) · The Crop Journal · doi:10.1016/j.cj.2021.03.015 · Cited by 269
David, E., Madec, S., Sadeghi-Tehran, P., Aasen, H., Zheng, B., Liu, S., Kirchgessner, N., Ishikawa, G., Nagasawa, K., Badhon, M. A., et al. (2020) · Plant Phenomics · doi:10.34133/2020/3521852 · Cited by 277
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
AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images
Ubbens, J. R., Ayalew, T. W., Shirtliffe, S., Josuttes, A., Pozniak, C., & Stavness, I. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-65414-6_27 · Cited by 15
Self-citation
Metadata
- Authors
- Ubbens, J. R., & Stavness, I.
- Venue
- Frontiers in Plant Science
- Publisher
- Frontiers Media SA
- Published
- 2017-07-07
- Volume
- 8
- Pages
- 1190-1190
- ISSN
- 1664-462X (ISSN-L)
- OpenAlex
- W2733608569
- Cited by
- 371
- References
- 30
- FWCI
- 28.12
- Type
- journal article
- Language
- en
- Open access
- gold
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