Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1185961
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dc.contributor.authorBHARTI, D.
dc.contributor.authorFARIA, L. N. de
dc.contributor.authorKOENIGKAN, L. V.
dc.contributor.authorGEBLER, L.
dc.contributor.authorRUFATO, A. de R.
dc.contributor.authorSANTOS, T. T.
dc.date.accessioned2026-03-31T17:55:08Z-
dc.date.available2026-03-31T17:55:08Z-
dc.date.created2026-03-31
dc.date.issued2026
dc.identifier.citationAgriculture, v. 16, n. 7, 764, 2026.
dc.identifier.issn2077-0472
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1185961-
dc.descriptionAccurate and spatially resolved yield estimation is a critical requirement for precision agriculture and orchard management. This paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–visual-inertial odometry (VIO) fusion, deep learning-based object detection, multi-frame tracking, three-dimensional triangulation, and incremental factor-graph optimization. Camera poses are obtained using ZED GNSS–VIO fusion and subsequently refined using an iSAM2-based nonlinear smoothing approach that incorporates strong relative-motion constraints and soft global ENU (East-North-Up) translation priors. Apples are detected using a YOLO-based model and associated across frames via CoTracker3, enabling robust multi-view landmark reconstruction. Reprojection factors and landmark priors are incorporated into a unified nonlinear factor graph to jointly optimize camera trajectories and 3D apple positions. The reconstructed apples are spatially aggregated into a grid-based mass map, where individual fruit volumes are estimated assuming spherical geometry and converted to mass using density models. The resulting ENU-referenced yield plot provides a structured representation of orchard production variability. Experimental results demonstrate significant reductions in reprojection error after optimization and improved global consistency of the trajectory, leading to stable and spatially coherent 3D reconstructions. The proposed pipeline bridges perception, geometry, and optimization, providing a scalable solution for orchard-scale yield mapping and decision support in precision agriculture.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectOdometria visual
dc.subjectReconstrução 3D
dc.subjectEstimativa de rendimento
dc.subjectISAM2
dc.subjectSLAM
dc.subjectVisual odometry
dc.subject3D reconstruction
dc.subjectYield estimation
dc.titleGeo-referenced factor-graph SLAM for orchard-scale 3D apple reconstruction and yield estimation.
dc.typeArtigo de periódico
dc.subject.thesagroAgricultura de Precisão
dc.subject.thesagroPomar
dc.subject.thesagroMaçã
dc.subject.nalthesaurusPrecision agriculture
dc.subject.nalthesaurusYield monitoring
dc.subject.nalthesaurusApples
riaa.ainfo.id1185961
riaa.ainfo.lastupdate2026-03-31
dc.identifier.doihttps://doi.org/10.3390/agriculture16070764
dc.contributor.institutionDHEERAJ BHARTI; LILIAN NOGUEIRA DE FARIA; LUCIANO VIEIRA KOENIGKAN, CNPTIA; LUCIANO GEBLER, CNPUV; ANDREA DE ROSSI, CNPUV; THIAGO TEIXEIRA SANTOS, CNPTIA.
Appears in Collections:Artigo em periódico indexado (CNPTIA)

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