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resumen

Resumen
Nowadays, in agronomic systems it is possible to make a variable management of inputs to improve the efficiency of agronomic industry and optimize the logistics of the harvesting process. In this way, it was proposed for sugarcane culture the use of remote sensing tools and computational methods to identify useful areas in the cultivated lands. The objective was to use these areas to make variable management of the crop. When at the moment of harvesting [ver mas...]
dc.contributor.authorSolano, Agustín
dc.contributor.authorKemerer, Alejandra Cecilia
dc.contributor.authorHadad, Alejandro Javier
dc.date.accessioned2023-07-18T17:49:15Z
dc.date.available2023-07-18T17:49:15Z
dc.date.issued2016
dc.identifier.issn1742-6596
dc.identifier.otherhttps://doi.org/10.1088/1742-6596/705/1/012025
dc.identifier.urihttp://hdl.handle.net/20.500.12123/14770
dc.identifier.urihttps://iopscience.iop.org/article/10.1088/1742-6596/705/1/012025
dc.descriptionTrabajo presentado al 20th Argentinean Bioengineering Society Congress, SABI 2015 (XX Congreso Argentino de Bioingeniería y IX Jornadas de Ingeniería Clínica)28–30 October 2015, San Nicolás de los Arroyos, Argentinaes_AR
dc.description.abstractNowadays, in agronomic systems it is possible to make a variable management of inputs to improve the efficiency of agronomic industry and optimize the logistics of the harvesting process. In this way, it was proposed for sugarcane culture the use of remote sensing tools and computational methods to identify useful areas in the cultivated lands. The objective was to use these areas to make variable management of the crop. When at the moment of harvesting the sugarcane there are fallen stalks, together with them some strange material (vegetal or mineral) is collected. This strange material is not millable and when it enters onto the sugar mill it causes important looses of efficiency in the sugar extraction processes and affects its quality. Considering this issue, the spectral response of sugarcane plants in aerial multispectral images was studied. The spectral response was analyzed in different bands of the electromagnetic spectrum. Then, the aerial images were segmented to obtain homogeneous regions useful for producers to make decisions related to the use of inputs and resources according to the variability of the system (existence of fallen cane and standing cane). The obtained segmentation results were satisfactory. It was possible to identify regions with fallen cane and regions with standing cane with high precision rates.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherIOP Sciencees_AR
dc.rightsinfo:eu-repo/semantics/openAccesses_AR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/es_AR
dc.sourceJournal of Physics: Conference Series 705 : 012025. (2016)es_AR
dc.subjectCaña de Azúcares_AR
dc.subjectSugar Caneeng
dc.subjectTeledetecciónes_AR
dc.subjectRemote Sensingeng
dc.subjectImágenes Multiespectraleses_AR
dc.subjectMultispectral Imageryeng
dc.subjectManejo del Cultivoes_AR
dc.subjectCrop Managementeng
dc.titleProcessing Pipeline of Sugarcane Spectral Response to Characterize the Fallen Plants Phenomenones_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.rights.licenseCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)es_AR
dc.description.origenEEA Paranáes_AR
dc.description.filFil: Solano, A. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Grupo de Investigación en Inteligencia Artificial; Argentinaes_AR
dc.description.filFil: Kemerer, Alejandra Cecilia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná. Grupo Recursos Naturales y Factores Abióticos; Argentinaes_AR
dc.description.filFil: Kemerer, Alejandra Cecilia. IUniversidad Nacional de Entre Ríos. Facultad de Ciencias Agrarias; Argentinaes_AR
dc.description.filFil: Hadad, Alejandro Javier. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Grupo de Investigación en Inteligencia Artificial; Argentinaes_AR
dc.subtypecientifico


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