Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1185942
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dc.contributor.authorSANTOS, T. T.
dc.contributor.authorFARIA, L. N. de
dc.contributor.authorGEBLER, L.
dc.date.accessioned2026-03-31T14:48:52Z-
dc.date.available2026-03-31T14:48:52Z-
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. 427-434.
dc.identifier.isbn978-85-86481-94-9
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1185942-
dc.descriptionComputer vision techniques for fruit detection and tracking are crucial for agricultural automation, yet most current datasets lack temporally consistent annotations needed for reliable tracking. Here we present Semear MAppleT FW, a dataset for apple detection and tracking in modern fruiting wall systems. The dataset comprises six video sequences of 100 frames each, captured by two RGB-D stereo cameras mounted on a tractor traversing orchard rows. Unlike previous datasets, Semear MAppleT FW features wide-angle images capturing entire tree lengths, ensuring complete canopy visibility within the field of view. To date, we provide over 53,000 bounding box annotations for 1,267 unique apple instances with temporal consistency across frames, stereo image pairs with known baseline calibration, and 3D reconstruction data. Our annotation method leverages structure-from-motion to estimate fruit positions in 3D space, enabling accurate tracking even when fruits are occluded by branches, leaves, or other fruits. The dataset includes visibility flags for each annotation, distinguishing between visible and occluded fruits. This approach maintains spatial consistency of annotations across frames while significantly reducing manual annotation workload. Semear MAppleT FW provides a valuable resource for developing artificial intelligence systems for automated yield estimation, fruit growth monitoring, and robotic harvesting in commercial orchards.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectProdução de maçãs
dc.subjectDetecção de fruta
dc.subjectRastreabilidade de frutas
dc.subjectAgricultura digital
dc.subjectApples Production
dc.subjectFruit Detection
dc.subjectFruit Tracking
dc.subjectDigital Agriculture
dc.titleSemear MAppleT FW: a dataset for apple detection and tracking in orchards under fruiting wall training system.
dc.typeArtigo em anais e proceedings
dc.subject.thesagroPomar
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.id1185942
riaa.ainfo.lastupdate2026-03-31
dc.contributor.institutionTHIAGO TEIXEIRA SANTOS, CNPTIA; LILIAN NOGUEIRA DE FARIA; LUCIANO GEBLER, CNPUV.
Appears in Collections:Artigo em anais de congresso (CNPTIA)

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