dc.creatorFernandez-Gallego, Jose A.
dc.creatorKefauver, Shawn C.
dc.creatorGuti?rrez, Nieves A.
dc.date2022-03-30T16:36:44Z
dc.date2022-03-30T16:36:44Z
dc.date2021-09-13
dc.date.accessioned2023-08-31T19:13:56Z
dc.date.available2023-08-31T19:13:56Z
dc.identifierJ. A. Fernandez-Gallego, S. C. Kefauver, N. A. Guti?rrez, M. T. Nieto-Taladriz and J. L. Araus, "Implications of Very Deep Super-Resolution (VDSR) on RGB imagery for grain yield assessment in wheat," 2020 Virtual Symposium in Plant Omics Sciences (OMICAS), 2020, pp. 1-5, doi: 10.1109/OMICAS52284.2020.9535654.
dc.identifier978-1-6654-3331-0
dc.identifierhttps://ieeexplore.ieee.org/document/9535654/authors
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8556554
dc.descriptionRGB imagery has been widely used for crop management practices and phenotyping applications in recent years. Although RGB wavelengths (400-700 nm) are not able to capture all essential plant data (such as with full ultraviolet, near and long infrared wavelength coverage), RGB cameras are the most common types of cameras and are among the versatile imaging devices for proximal remote sensing applications. Deep learning strategies have improved a wide range of processes and deep learning concepts can be included in many applications. This work uses the Very Deep Super-Resolution (VDSP) technique to improve low-resolution RGB images in order to study grain yield assessment in wheat using vegetation indexes. The results show no significant differences between indexes calculated from low-resolution images and low-resolution images processed using VDSP with grain yield.
dc.descriptionUniversidad de Ibagu?
dc.languageen
dc.publisher2020 Virtual Symposium in Plant Omics Sciences, OMICAS 2020
dc.subjectDeep learning
dc.subjectSuper resolution
dc.subjectVegetation mapping
dc.subjectCrops
dc.subjectCameras
dc.subjectIndexes
dc.subjectRemote sensing
dc.titleImplications of Very Deep Super-Resolution (VDSR) on RGB imagery for grain yield assessment in wheat
dc.typeArticle


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