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dc.contributor.authorChang, Anjin
dc.contributor.authorJung, Jinha
dc.contributor.authorYeom, Junho
dc.contributor.authorMaeda, Murilo
dc.contributor.authorLandivar, Juan
dc.contributor.authorEnciso, Juan
dc.contributor.authorAvila, Carlos A.
dc.contributor.authorAnciso, Juan R.
dc.date.accessioned2022-03-22T20:03:00Z
dc.date.available2022-03-22T20:03:00Z
dc.date.issued2021-02-09
dc.identifier.citationChang, A., Jung, J., Yeom, J., Maeda, M.M., Landivar, J.A., Enciso, J.M., Avila, C.A. and Anciso, J.R., 2021. Unmanned aircraft system-(UAS-) based high-throughput phenotyping (HTP) for tomato yield estimation. Journal of Sensors, 2021.en_US
dc.identifier.urihttps://hdl.handle.net/1969.6/90303
dc.description.abstractYield prediction and variety selection are critical components for assessing production and performance in breeding programs and precision agriculture. Since plants integrate their genetics, surrounding environments, and management conditions, crop phenotypes have been measured over cropping seasons to represent the traits of varieties. These days, UAS (unmanned aircraft system) provides a new opportunity to collect high-quality images and generate reliable phenotypic data efficiently. Here, we propose high-throughput phenotyping (HTP) from multitemporal UAS images for tomato yield estimation. UAS-based RGB and multispectral images were collected weekly and biweekly, respectively. The shape of the features of tomatoes such as canopy cover, canopy, volume, and vegetation indices derived from UAS imagery was estimated throughout the entire season. To extract time-series features from UAS-based phenotypic data, crop growth and growth rate curves were fitted using mathematical curves and first derivative equations. Time-series features such as the maximum growth rate, day at a specific event, and duration were extracted from the fitted curves of different phenotypes. The linear regression model produced high values even with different variable selection methods: all variables (0.79), forward selection (0.7), and backward selection (0.77). With factor analysis, we figured out two significant factors, growth speed and timing, related to high-yield varieties. Then, five time-series phenotypes were selected for yield prediction models explaining 65 percent of the variance in the actual harvest. The phenotypic features derived from RGB images played more important roles in prediction yield. This research also demonstrates that it is possible to select lower-performing tomato varieties successfully. The results from this work may be useful in breeding programs and research farms for selecting high-yielding and disease-/pest-resistant varieties.en_US
dc.language.isoen_USen_US
dc.publisherHindawien_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectunmanned aircraft systemsen_US
dc.subjectuasen_US
dc.subjecthigh throughput phenotypingen_US
dc.subjecthtpen_US
dc.subjecttomatoen_US
dc.titleUnmanned aircraft system- (UAS-) based high-throughput phenotyping (HTP) for tomato yield estimationen_US
dc.typeArticleen_US
dc.creator.orcidhttps://orcid.org/0000-0001-8475-8836en_US
dc.creator.orcidhttp://orcid.org/0000-0001-7571-1155en_US
dc.creator.orcidhttp://orcid.org/0000-0001-6870-3771en_US
dc.creator.orcidhttp://orcid.org/0000-0002-2697-5258en_US
dc.creator.orcidhttp://orcid.org/0000-0001-5984-6826en_US
dc.creator.orcidhttps://orcid.org/0000-0001-8475-8836
dc.creator.orcidhttps://orcid.org/0000-0001-7571-1155
dc.creator.orcidhttps://orcid.org/0000-0001-6870-3771
dc.creator.orcidhttps://orcid.org/0000-0002-2697-5258
dc.creator.orcidhttps://orcid.org/0000-0001-5984-6826
dc.creator.orcidhttps://orcid.org/0000-0001-8475-8836
dc.creator.orcidhttps://orcid.org/0000-0001-7571-1155
dc.creator.orcidhttps://orcid.org/0000-0001-6870-3771
dc.creator.orcidhttps://orcid.org/0000-0002-2697-5258
dc.creator.orcidhttps://orcid.org/0000-0001-5984-6826https://orcid.org/0000-0001-8475-8836
dc.creator.orcidhttp://orcid.org/0000-0001-7571-1155
dc.creator.orcidhttp://orcid.org/0000-0001-6870-3771
dc.creator.orcidhttp://orcid.org/0000-0002-2697-5258
dc.creator.orcidhttp://orcid.org/0000-0001-5984-6826
dc.identifier.doihttps://doi.org/10.1155/2021/8875606


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International