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dc.contributor.authorLUZ, J. S.
dc.contributor.authorOLIVEIRA, M. C. de
dc.contributor.authorPEREIRA, F. de M.
dc.contributor.authorARAÚJO, F. H. D. de
dc.contributor.authorMAGALHÃES, D. M. V.
dc.date.accessioned2025-04-04T18:49:46Z-
dc.date.available2025-04-04T18:49:46Z-
dc.date.created2025-04-04
dc.date.issued2024
dc.identifier.citationJournal of Internet Services and Applications, v. 15, n.1, p. 548–560, 2024.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1174543-
dc.descriptionRegular management practices are crucial to assessing colonies’ conditions and implementing measures to improve their strength. However, constant revisions can induce stress and even contribute to swarm loss. Therefore, effective management that considers the well-being of the bees is necessary. In order to assist the beekeeper in managing the hives, this study proposes a noninvasive approach integrating Apis mellifera L., 1758 (Hymenoptera: Apidae) colony sound processing with machine learning and deep learning techniques to identify colony strength, essential for the productivity of apiculture.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectApicultura de precisão
dc.subjectAprendizado de máquina
dc.subjectExtração de características
dc.subjectProcessamento de áudio
dc.titleCepstral and deep features for Apis mellifera hive strength classification.
dc.typeArtigo de periódico
riaa.ainfo.id1174543
riaa.ainfo.lastupdate2025-04-04
dc.identifier.doi10.5753/jisa.2024.4015
dc.contributor.institutionJEDERSON S. LUZ, FEDERAL UNIVERSITY OF PIAUÍ; MYLLENA C. DE OLIVEIRA, FEDERAL UNIVERSITY OF PIAUÍ; FABIA DE MELLO PEREIRA, CPAMN; FLÁVIO H. D. DE ARAÚJO, FEDERAL UNIVERSITY OF PIAUÍ; DEBORAH M. V. MAGALHÃES, FEDERAL UNIVERSITY OF PIAUÍ.
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