Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1185950
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dc.contributor.authorSANTOS, T. T.
dc.contributor.authorKOENIGKAN, L. V.
dc.contributor.authorGEBLER, L.
dc.date.accessioned2026-03-31T15:49:00Z-
dc.date.available2026-03-31T15:49:00Z-
dc.date.created2026-03-31
dc.date.issued2025
dc.identifier.citationIn: WORKSHOP CIENTÍFICO DO CENTRO DE CIÊNCIA PARA O DESENVOLVIMENTO EM AGRICULTURA DIGITAL – SEMEAR DIGITAL, 2., 2025, Campinas. Anais [...]. Piracicaba: ESALQ/USP, 2025. p. 468-474.
dc.identifier.isbn978-85-86481-94-9
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1185950-
dc.descriptionPrecision agriculture applications at the plant level require high-resolution, detailed imaging that cannot be adequately provided by satellite-based remote sensing due to insufficient resolution and suboptimal viewing angles. While Unmanned Aerial Vehicles (UAVs) offer improved capabilities, their downward-facing perspective and navigation challenges in orchards limit their effectiveness for fruit monitoring and anomalies detection. To address these limitations, we present SEEmear, a novel ground-based proximal sensing system for large-scale geo-referenced stereo imaging of orchard environments. The system integrates high-performance embedded computing, wide-angle global shutter RGB-D cameras, and precision RTK GNSS positioning, enabling simultaneous imaging of both sides of orchard rows from close proximity. SEEmear's 110° field-of-view cameras capture entire tree structures even at distances of 1 meter, while global shutter sensors eliminate motion artifacts essential for moving platforms. We tested the system in apple orchards, collecting comprehensive geo-referenced RGB-D imagery across 1.50 ha in approximately 40 minutes per session. The resulting dataset supports advanced applications including depth estimation, 3D reconstruction, background filtering, and object segmentation. The adaptable platform integrates with various ground vehicles and provides substantial data storage and processing capabilities needed for hardware-accelerated AI algorithms. SEEmear addresses the critical need for high-quality proximal sensing data in precision agriculture research, supporting applications in fruit detection, tracking, yield mapping, autonomous navigation, and field robotics.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectImagem de pomares
dc.titleSEEmear: a system for large scale geo-referenced stereo imaging of orchards.
dc.typeArtigo em anais e proceedings
dc.subject.thesagroPomar
dc.subject.thesagroMaçã
dc.subject.thesagroAgricultura de Precisão
dc.subject.nalthesaurusPrecision agriculture
dc.subject.nalthesaurusApples
dc.description.notesOrganização: Silvia Maria Fonseca Silveira Massruhá, Durval Dourado Neto, Luciana Alvim Santos Romani, Jayme Garcia Arnal Barbedo, Édson Luis Bolfe, Ivan Bergier, Maria Angelica de Andrade Leite, Vitor Del Alamo Guarda, Catarina Barbosa Careta.
riaa.ainfo.id1185950
riaa.ainfo.lastupdate2026-03-31
dc.contributor.institutionTHIAGO TEIXEIRA SANTOS, CNPTIA; LUCIANO VIEIRA KOENIGKAN, CNPTIA; LUCIANO GEBLER, CNPUV.
Aparece nas coleções:Artigo em anais de congresso (CNPTIA)

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