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dc.contributor.authorRamos, Caio C. O. [UNESP]
dc.contributor.authorClerice, Guilherme A. M. [UNESP]
dc.contributor.authorCastro, Bruno A. [UNESP]
dc.contributor.authorSilva Filho, Nelson M. [UNESP]
dc.contributor.authorUlson, Jose Alfredo C. [UNESP]
dc.contributor.authorIEEE
dc.date.accessioned2018-11-26T17:15:26Z
dc.date.available2018-11-26T17:15:26Z
dc.date.issued2016-01-01
dc.identifier.citation2016 Ieee International Conference On Automatica (ica-acca). New York: Ieee, 5 p., 2016.
dc.identifier.urihttp://hdl.handle.net/11449/162276
dc.description.abstractThe strong rising on demand for agricultural crop quantity and quality, beyond the growing concern of non-point pollution, are requiring higher levels on agricultural production systems efficiency and environmental safety. Measurement of the nitrogen (N) on cultivation is one successful method of increase the agricultural production systems. Nitrogen application can increase productivity, decrease environmental impacts and also reduce costs by using the correct amount of nitrogen fertilizers. Recently, optical sensors applied to nitrogen measurement are attracting interest of several researches as a technique to enhance the productivity of sugar cane plants. However, the accuracy of measurements of reflectance still needs to be improved. This work proposes a new identifying approach based on near infrared reflectance (NIR) spectroscopy real time sensor using artificial intelligence techniques in order to improve the accuracy of the nitrogen measurements in sugar cane. An optical sensor is used to estimate the amount of nitrogen by reflectance measurement of sugar cane plants in the early stages of growing and the data is post-processed using intelligent systems. The results obtained using self-organizing map (SOM) presented better perform than other techniques for nitrogen content identification in sugar cane crops confirming the efficiency of intelligent approach in the real time.en
dc.format.extent5
dc.language.isoeng
dc.publisherIeee
dc.relation.ispartof2016 Ieee International Conference On Automatica (ica-acca)
dc.sourceWeb of Science
dc.subjectArtificial Intelligence
dc.subjectNear-Infrared Spectroscopy
dc.subjectNitrogen Content Identification
dc.subjectSugar Cane
dc.titleArtificial Intelligence Techniques and Near-Infrared Spectroscopy for Nitrogen Content Identification in Sugar Cane Cropsen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIeee
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationUniv Estadual Paulista, Dept Elect Engn, Bauru, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Dept Elect Engn, Bauru, SP, Brazil
dc.identifier.wosWOS:000390556300001
dc.rights.accessRightsAcesso aberto
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