Title:	Cepstral and deep features for Apis mellifera hive strength classification.
Authors:	LUZ, J. S. OLIVEIRA, M. C. de PEREIRA, F. de M. ARAÚJO, F. H. D. de MAGALHÃES, D. M. V.
Affiliation:	JEDERSON 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.
Date Issued:	2024
Citation:	Journal of Internet Services and Applications, v. 15, n.1, p. 548–560, 2024.
Description:	Regular 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.
Keywords:	Apicultura de precisão Aprendizado de máquina Extração de características Processamento de áudio
DOI:	10.5753/jisa.2024.4015
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CPAMN)