Reconhecimento de padrões sazonais em colônias de abelhas Apis mellifera

• Ano de defesa: 2018
• Autor(a) principal: Maciel, Felipe Anderson Oliveira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Teleinformática; Mineração de dados (Computação); Reconhecimento de padrões; Abelha africanizada; Abelha - Criação; Colméia - Manejo; Clustering; Apis mellifera; Precision beekeeping; Data mining; Pattern recognition; Honey bee
• Link de acesso: http://www.repositorio.ufc.br/handle/riufc/36019

As the main pollinating agent, bees are essential to the production of food for mankind and to the maintenance of the ecosystem. Among the crops used for human consumption, 75% rely on pollination. Aligning to a current concern with bees survival, this dissertation aims to find out patterns of Apis mellifera colonies in order to assist the beekeeper in the management and maintenance of his hives. Our methodology consisted in the application of a clustering technique in two real datasets of hives with data of temperature, humidity and weight. From the application of the Calinski-Harabasz index and the K-means algorithm, we have identified coherent patterns associated with the transitions between the seasons. In addition, we conclude that the strongest colony is most efficient in trying to maintain the microclimate of the hive during the winter.