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dc.contributor.authorSPERANZA, E. A.
dc.contributor.authorGREGO, C. R.
dc.contributor.authorSANTOS, T. T.
dc.contributor.authorRODRIGUES, G. C.
dc.contributor.authorINAMASU, R. Y.
dc.date.accessioned2026-08-04T16:48:57Z-
dc.date.available2026-08-04T16:48:57Z-
dc.date.created2026-08-04
dc.date.issued2026
dc.identifier.citationIn: INTERNATIONAL CONFERENCE ON PRECISION AGRICULTURE, 17., CONGRESSO BRASILEIRO DE AGRICULTURA DE PRECISÃO DIGITAL, 11., 2026, Porto Alegre. Proceedings [...]. Monticello: International Society of Precision Agriculture, 2026.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1188903-
dc.descriptionPrecision agriculture in mountainous perennial crops faces unique implementation challenges due to steep topography and restricted accessibility, which limit traditional methods for delineating management zones (MZs). To overcome this, this study evaluated the integration of multiresolution remote sensing data to delineate and validate temporally stable MZs in topographically complex coffee fields in Caconde, São Paulo, Brazil. The methodology combined a historical timeseries of multi-spectral orbital imagery with high-resolution suborbital data acquired via Remotely Piloted Aircraft (RPAs). Stable spatial patterns were mapped by analyzing temporal vegetation indices alongside time-stable variables, such as altimetry, slope, and soil-plant classifications. The clustering quality was evaluated using Silhouette Width (SW) and Variance Reduction (VR) metrics. Results showed a trade-off: while VR suggested a higher number of MZs, SW favored fewer, more cohesive zones (2 to 3 MZs) derived from pixels with high plant incidence (80-90%). The MZs selected by SW proved more operationally feasible for hard-to-reach areas and were successfully validated by altimetry data, presenting distinct topographic profiles. Balancing statistical clustering metrics with topographical validation provides a reliable and cost-effective proxy for establishing practical MZs in steep-slope coffee farming, supporting sustainable sitespecific interventions.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectAgricultura digital
dc.subjectÍndice de vegetação
dc.subjectDigital agriculture
dc.titleDelineation of management zones for the adoption of precision and digital agriculture in steep-sloped arabica coffee production áreas.
dc.typeArtigo em anais e proceedings
dc.subject.thesagroAgricultura de Precisão
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroCafé
dc.subject.thesagroCoffea Arábica
dc.subject.nalthesaurusPrecision agriculture
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusVegetation index
dc.description.notesICPA 2026, ConBAP 2026.eng
dc.format.extent21O p.
riaa.ainfo.id1188903
riaa.ainfo.lastupdate2026-08-04
dc.contributor.institutionEDUARDO ANTONIO SPERANZA, CNPTIA; CELIA REGINA GREGO, CNPTIA; THIAGO TEIXEIRA SANTOS, CNPTIA; GUSTAVO COSTA RODRIGUES, CNPTIA; RICARDO YASSUSHI INAMASU, CNPDIA.
Aparece en las colecciones:Artigo em anais de congresso (CNPTIA)

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