resumen
Abstract
In this paper we present a implementation and characterization the status of sugarcane plantations based on the analysis of multispectral aerial images. Currently there are no precise techniques to estimate objectively the cane area fall or overturned, and this causes significant losses in crop productivity and industrialization. For the realization of this work was made a dataset benchmark images, and implemented a software from which were obtained
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dc.contributor.author | Solano, Agustín | |
dc.contributor.author | Schneider, Gerardo | |
dc.contributor.author | Kemerer, Alejandra Cecilia | |
dc.contributor.author | Hadad, Alejandro Javier | |
dc.date.accessioned | 2023-07-18T17:25:49Z | |
dc.date.available | 2023-07-18T17:25:49Z | |
dc.date.issued | 2013 | |
dc.identifier.issn | 1742-6596 | |
dc.identifier.other | https://doi.org/10.1088/1742-6596/477/1/012016 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12123/14769 | |
dc.identifier.uri | https://iopscience.iop.org/article/10.1088/1742-6596/477/1/012016 | |
dc.description | Trabajo presentado al 19th Argentinean Bioengineering Society Congress (SABI 2013), 4–6 September 2013, Tucumán, Argentina | es_AR |
dc.description.abstract | In this paper we present a implementation and characterization the status of sugarcane plantations based on the analysis of multispectral aerial images. Currently there are no precise techniques to estimate objectively the cane area fall or overturned, and this causes significant losses in crop productivity and industrialization. For the realization of this work was made a dataset benchmark images, and implemented a software from which were obtained indicators related to agronomic phenomenon, and analyzes of the data generated. In addition was used Principal Component Analysis to visualize and integrate statistical texture features. The results indicate the statistical features used characterize partially the sugar cane phenomena and suggest include another texture focus to complement this feature set, previous to built an cane identification process. | eng |
dc.format | application/pdf | es_AR |
dc.language.iso | eng | es_AR |
dc.publisher | IOP Science | es_AR |
dc.rights | info:eu-repo/semantics/openAccess | es_AR |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | es_AR |
dc.source | Journal of Physics: Conference Series 477 : 012016 (2013) | es_AR |
dc.subject | Caña de Azúcar | es_AR |
dc.subject | Sugar Cane | eng |
dc.subject | Imágenes Multiespectrales | es_AR |
dc.subject | Multispectral Imagery | eng |
dc.subject | Productividad | es_AR |
dc.subject | Productivity | eng |
dc.title | Characterization of multispectral aerial images of sugarcane | es_AR |
dc.type | info:ar-repo/semantics/artículo | es_AR |
dc.type | info:eu-repo/semantics/article | es_AR |
dc.type | info:eu-repo/semantics/publishedVersion | es_AR |
dc.rights.license | Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) | es_AR |
dc.description.origen | EEA Paraná | es_AR |
dc.description.fil | Fil: Solano, A. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Grupo de Investigación en Inteligencia Artificial; Argentina | es_AR |
dc.description.fil | Fil: Schneider, Gerardo. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Grupo de Investigación en Inteligencia Artificial; Argentina | es_AR |
dc.description.fil | Fil: Kemerer, Alejandra Cecilia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná. Grupo Recursos Naturales y Factores Abióticos; Argentina | es_AR |
dc.description.fil | Fil: Kemerer, Alejandra Cecilia. IUniversidad Nacional de Entre Ríos. Facultad de Ciencias Agrarias; Argentina | es_AR |
dc.description.fil | Fil: Hadad, Alejandro Javier. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Grupo de Investigación en Inteligencia Artificial; Argentina | es_AR |
dc.subtype | cientifico |
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