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dc.contributor.authorHEIDEKER, A.
dc.contributor.authorSPERANZA, E. A.
dc.contributor.authorFERREIRA, E. J.
dc.contributor.authorSILVA, D.
dc.contributor.authorKAMIENSKI, C.
dc.contributor.authorBIANCHI, R.
dc.date.accessioned2026-08-04T17:49:23Z-
dc.date.available2026-08-04T17:49:23Z-
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/1188914-
dc.descriptionAbstract. Satellite remote sensing is a key data source for precision agriculture, enabling vegetation indices such as NDMI and EVI to monitor vegetation health and soil water stress over large areas. Its practical use, however, is often constrained by manual image selection, downloading, and preprocessing workflows, which are time-consuming, hard to reproduce, and ill-suited to building the large, multi-temporal datasets that machine learning requires. This study presents an automated system for satellite image acquisition and preprocessing that enables scalable dataset generation for soil water stress monitoring. The system retrieves historical and current Sentinel-2 multispectral imagery through the Copernicus API, guided by predefined spatial and temporal parameters. Areas of interest are defined as multiple polygons in GeoJSON files representing individual agricultural sectors, which are grouped within a simplified bounding area to reduce redundant queries and improve retrieval efficiency. By eliminating manual scene inspection, the system reduces human effort while ensuring consistency and reproducibility, enabling systematic expansion of monitored regions and supporting the creation of high-quality datasets for training and deploying machine learning models in irrigation management. Its modular design accommodates the integration of additional satellite missions, spectral indices, and preprocessing methods in the future
dc.language.isopor
dc.rightsopenAccess
dc.subjectSentinel-2
dc.subjectÍndice de vegetação
dc.subjectAprendizado de máquina
dc.subjectEstresse hídrico do solo
dc.subjectMonitoramento da cultura do café
dc.subjectSatellite remote sensing
dc.subjectVegetation indices
dc.subjectMachine learning datasets
dc.subjectCloud masking
dc.subjectSoil water stress
dc.subjectCoffee crop monitoring
dc.titleSatellite imagery to machine learning datasets: an automated system for soil water stress monitoring in agriculture.
dc.typeArtigo em anais e proceedings
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroAgricultura de Precisão
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusVegetation index
dc.subject.nalthesaurusWater stress
dc.subject.nalthesaurusCrop management
dc.description.notesICPA 2026, ConBAP 2026. Na publicação: Eduardo Speranza, Ednaldo Pereira.
dc.format.extent29 p.
riaa.ainfo.id1188914
riaa.ainfo.lastupdate2026-08-04
dc.contributor.institutionALEXANDRE HEIDEKER, CENTRO UNIVERSITÁRIO FEI
dc.contributor.institutionEDUARDO ANTONIO SPERANZA, CNPTIApor
dc.contributor.institutionEDNALDO JOSE FERREIRA, CNPDIApor
dc.contributor.institutionDENER SILVA, CENTRO UNIVERSITÁRIO FEIpor
dc.contributor.institutionCARLOS KAMIENSKIpor
dc.contributor.institutionREINALDO BIANCHI, CENTRO UNIVERSITÁRIO FEI.por
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