Reconhecimento de padrões sazonais em colônias de abelhas Apis mellifera
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...
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| Formato: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | Brasil |
| Recursos: | Universidade Federal do Ceará (UFC) |
| Repositorio: | Repositório Institucional da Universidade Federal do Ceará (UFC) |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufc.br:riufc/36019 |
| Acesso em linha: | http://www.repositorio.ufc.br/handle/riufc/36019 |
| Access Level: | acceso abierto |
| Palavra-chave: | 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 |
| Resumo: | 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. |
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