Examinando por Materia "Remote sensing"
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- PublicaciónSólo datosCrop phenotyping in a context of Global Change: what to measure and how to do it(Journal of Integrative Plant Biology, 2022-01-01) Araus, Jose Luis; Kefauver, Shawn Carlisle; Vergara‐Díaz, Omar; Gracia‐Romero, Adrian; Zahra Rezzouk, Fatima; Segarra, Joel; Buchaillot, Maria Luisa; Chang‐Espino, Melissa; Vatter, Thomas; Sanchez‐Bragado, Rut; Fernandez‐Gallego, José Armando; Serret, Maria Dolores; Bort, JordiHigh‐throughput crop phenotyping, particularlyunderfield conditions, is nowadays perceivedas a key factor limiting crop genetic advance.Phenotyping not only facilitates conventionalbreeding, but it is necessary to fully exploit thecapabilities of molecular breeding, and it can beexploited to predict breeding targets for the yearsahead at the regional level through more ad-vanced simulation models and decision supportsystems. In terms of phenotyping, it is necessaryto determined which selection traits are relevantin each situation, and which phenotyping tools/methods are available to assess such traits. Re-mote sensing methodologies are currently themost popular approaches, even when lab‐basedanalyses are still relevant in many circumstances.On top of that, data processing and automation,together with machine learning/deep learning arecontributing to the wide range of applications forphenotyping. This reviewaddresses spectral andred–green–blue sensing as the most popular re-mote sensing approaches, alongside stable iso-tope composition as an example of a lab‐basedtool, and root phenotyping, which represents oneof the frontiers forfield phenotyping. Further, weconsider the two most promising forms of aerialplatforms (unmanned aerial vehicle and satellites)and some of the emerging data‐processingtechniques. The review includes three Boxesthat examine specificcasestudies.
- PublicaciónSólo datosImplications of Very Deep Super-Resolution (VDSR) on RGB imagery for grain yield assessment in wheat(2020 Virtual Symposium in Plant Omics Sciences, OMICAS 2020, 2021-09-13) Fernandez-Gallego, Jose A.; Kefauver, Shawn C.; Gutiérrez, Nieves A.RGB 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.