Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1170138
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dc.contributor.authorYANO, I. H.
dc.contributor.authorCARVALHO, M. L. de M
dc.contributor.authorMARCHIORI, L. F. S.
dc.contributor.authorSILVA, F. C. da
dc.date.accessioned2024-12-28T17:32:39Z-
dc.date.available2024-12-28T17:32:39Z-
dc.date.created2024-12-06
dc.date.issued2024
dc.identifier.citationIn: INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGY MANAGEMENT, 2024, 20., São Paulo. Anais do congresso. São Paulo: FEA USP, 2024.
dc.identifier.issn2448-1041
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1170138-
dc.descriptionProductivity estimates play a crucial role for sugarcane producers and sugar mills in planning production, aligning it with demand forecasts. Manual estimations demand considerable effort and time, prompting exploration into alternative productivity estimation methods such as aerial imaging using drones. Within imaging techniques, productivity estimation occurs indirectly through the analysis of vegetation indices. The widely recognized vegetation index, NDVI, necessitates costly near-infrared (NIR) cameras, making it inaccessible to many producers. Our approach utilized drone imagery captured by more affordable RGB cameras, which are feasible for a larger number of producers. We applied six regression models alongside a stacking model that amalgamated these six models for estimating sugarcane production using the eight RGB vegetation indices. Initial tests revealed a Mean Absolute Percentage Error (MAPE) of less than 13%. This level of accuracy is considered favorable when benchmarked against similar studies and presents encouraging prospects for future research.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectDrone
dc.subjectAprendizado de máquina
dc.subjectMachine learning
dc.titleAssessment of sugarcane production using regression models and RGB vegetation indices data.
dc.typeArtigo em anais e proceedings
dc.subject.thesagroCana de Açúcar
dc.subject.thesagroRendimento
dc.subject.nalthesaurusUnmanned aerial vehicles
dc.subject.nalthesaurusYields
dc.subject.nalthesaurusSugarcane
dc.description.notesCONTECSI USP 2024. Evento virtual.
dc.format.extent212 p.
riaa.ainfo.id1170138
riaa.ainfo.lastupdate2024-12-06
dc.identifier.doi10.5748/20CONTECSI/COM/AGB/7257
dc.contributor.institutionINACIO HENRIQUE YANO, CNPTIA, FACULDADE DE TECNOLOGIA DE SANTANA DE PARNAÍBA; MARIANA LOPES DE CARVALHO, FACULDADE DE TECNOLOGIA DE PIRACICABA; LUIS FERNANDO SANGLADE MARCHIORI, UNIVERSIDADE DE SÃO PAULO; FABIO CESAR DA SILVA, CNPTIA, FACULDADE DE TECNOLOGIA DE PIRACICABA.
Aparece nas coleções:Artigo em anais de congresso (CNPTIA)

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