Data Analysis Workflow For Experiments In Sugarcane Precision Agriculture

dc.creatorDriemeier C.E.
dc.creatorLing L.Y.
dc.creatorPontes A.O.
dc.creatorSanches G.M.
dc.creatorFranco H.C.J.
dc.creatorMagalhaes P.S.G.
dc.creatorFerreira J.E.
dc.date2014
dc.date2015-06-25T17:59:16Z
dc.date2015-11-26T14:57:37Z
dc.date2015-06-25T17:59:16Z
dc.date2015-11-26T14:57:37Z
dc.date.accessioned2018-03-28T22:09:25Z
dc.date.available2018-03-28T22:09:25Z
dc.identifier9781479942886
dc.identifierProceedings - 2014 Ieee 10th International Conference On Escience, Escience 2014. Institute Of Electrical And Electronics Engineers Inc., v. 1, n. , p. 163 - 168, 2014.
dc.identifier
dc.identifier10.1109/eScience.2014.10
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84919490460&partnerID=40&md5=04410c876dbedb58836c7e6f1684e122
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/87335
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/87335
dc.identifier2-s2.0-84919490460
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1255683
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionPrecision Agriculture (PA) comprises a set of tools to understand and manage inherent spatial variability within crop fields. PA relies on a variety of techniques to collect, analyze, process, and synthesize voluminous geo referenced data. However, prior to large-scale practice, PA requires a successful experimentation stage, which is the present stage of PA for the sugarcane system. This paper presents a data analysis workflow for PA experiments, including workflow application to a case study in a sugarcane area where an appreciable diversity of soil and plant attributes has been measured. Our data analysis workflow has basis on: i) removal of outliers, ii) representation of different data acquisition techniques on a common spatial grid, iii) estimation of typical 'noise' level in each measured attribute, iv) spatial autocorrelation analysis for each attribute, v) correlation analysis to identify related attributes, and vi) principal component analysis to reduce the dimensionality of the attribute space. By treating the diversity of measured attributes on a common ground, the proposed analysis workflow guides further experimentation as well as selection of data acquisition technologies suitable for large-scale sugarcane PA.
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dc.description168
dc.descriptionFAPESP 2011/028179; FAPESP; São Paulo Research Foundation
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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dc.languageen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relationProceedings - 2014 IEEE 10th International Conference on eScience, eScience 2014
dc.rightsfechado
dc.sourceScopus
dc.titleData Analysis Workflow For Experiments In Sugarcane Precision Agriculture
dc.typeActas de congresos


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