| dc.contributor.author | Krosney, Alexander E. | |
| dc.contributor.author | Sotoodeh, Parsa | |
| dc.contributor.author | Henry, Christopher J. | |
| dc.contributor.author | Beck, Michael A. | |
| dc.contributor.author | Bidinosti, Christopher P. | |
| dc.date.accessioned | 2023-07-17T20:19:18Z | |
| dc.date.available | 2023-07-17T20:19:18Z | |
| dc.date.issued | 2023-07-06 | |
| dc.identifier.citation | Krosney, Alexander E., Parsa Sotoodeh, Christopher J. Henry, Michael A. Beck, and Christopher P. Bidinosti. "Inside out: transforming images of lab-grown plants for machine learning applications in agriculture." Frontiers in Artificial Intelligence 6 (2023): Sec. AI in Food, Agriculture and Water. DOI: 10.3389/frai.2023.1200977. | en_US |
| dc.identifier.issn | 2624-8212 | |
| dc.identifier.uri | https://hdl.handle.net/10680/2087 | |
| dc.description.abstract | Machine learning tasks often require a significant amount of training data for the resultant network to perform suitably for a given problem in any domain. In agriculture, dataset sizes are further limited by phenotypical differences between two plants of the same genotype, often as a result of different growing conditions. Synthetically-augmented datasets have shown promise in improving existing models when real data is not available. | en_US |
| dc.description.sponsorship | "This work was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant program (Nos. RGPIN-2018-04088 and RGPIN-2020-06191), Compute Canada (now Digital Research Alliance of Canada) Resources for Research Groups competition (No. 1679), Western Economic Diversification Canada (No. 15453), and the Mitacs Accelerate Grant program (No. IT14120)." | en_US |
| dc.description.uri | https://www.frontiersin.org/articles/10.3389/frai.2023.1200977/full | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Frontiers | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Machine learning | en_US |
| dc.title | Inside out: transforming images of lab-grown plants for machine learning applications in agriculture | en_US |
| dc.type | Article | en_US |
| dc.identifier.doi | 10.3389/frai.2023.1200977 | en_US |
