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dc.contributor.authorCruz-Ramírez, M.-
dc.contributor.authorHervás-Martínez, César-
dc.contributor.authorJurado-Expósito, Montserrat-
dc.contributor.authorLópez Granados, Francisca-
dc.date.accessioned2013-10-17T09:26:02Z-
dc.date.available2013-10-17T09:26:02Z-
dc.date.issued2012-09-
dc.identifierdoi: 10.1016/j.eswa.2012.02.046-
dc.identifierissn: 0957-4174-
dc.identifier.citationExpert Systems with Applications 39(11): 10038-10048 (2012)-
dc.identifier.urihttp://hdl.handle.net/10261/84412-
dc.description.abstractOne of the objectives of conservation agriculture to reduce soil erosion in olive orchards is to protect the soil with cover crops between rows. Andalusian and European administrations have developed regulations to subsidise the establishment of cover crops between rows in olive orchards. Current methods to follow-up the cover crops systems by administrations consist of sampling and on ground visits of around 1% of the total olive orchards surface at any time from March to late June. This paper outlines a multi-objective neural network based method for the classification of olive trees (OT), bare soil (BS) and different cover crops (CC), using remote sensing data taken in spring and summer. The main findings of this paper are: (1) the proposed models performed well in all seasons (particularly during the summer, where only 48 pixels of CC are confused with BS and 10 of BS with CC with the best model obtained. This model obtained a 97.80% of global classification, 95.20% in the class with the worst classification rate and 0.9710 in the KAPPA statistics), and (2) the best-performing models could potentially decrease the number of complaints made to the Andalusian and European administrations. The complaints in question concern the poor performance of current on-ground methods to address the presence or absence of cover crops in olive orchards. © 2012 Elsevier Ltd. All rights reserved.-
dc.description.sponsorshipThis work was supported in part by the Spanish Inter-Ministerial Commission of Science and Technology under Project TIN2011–22794, the Spanish Minister of Science and Innovation by project AGL2011–30442-CO2–01 (FEDER), the European Regional Development fund and the “Junta de Andalucía” (Spain), under Project P2011-TIC-7508. M. Cruz-Ramírez’s research has been subsidized by the FPU Predoctoral Program (Spanish Ministry of Education and Science), grant reference AP2009–0487.-
dc.language.isoeng-
dc.publisherPergamon Press-
dc.rightsclosedAccess-
dc.titleA multi-objective neural network based method for cover crop identification from remote sensed data-
dc.typeArtículo-
dc.identifier.doi10.1016/j.eswa.2012.02.046-
dc.date.updated2013-10-17T09:26:02Z-
dc.description.versionPeer Reviewed-
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