Cited By
View all- Astuti PHegedűs BOleksa ABagi ZKusza S(2024)Buzzing with Intelligence: Current Issues in Apiculture and the Role of Artificial Intelligence (AI) to Tackle ItInsects10.3390/insects1506041815:6(418)Online publication date: 4-Jun-2024
Data Mining to Characterize Seasonal Patterns of Apis mellifera Honey Bee Colonies
Authors: 
Felipe Anderson O. Maciel, Antonio Rafael Braga, Rhaniel M. Xavier, Ticiana L. Coelho da Silva, Breno M. Freitas, 
Danielo G. GomesAuthors Info & Claims
Felipe Anderson O. Maciel, Antonio Rafael Braga, Rhaniel M. Xavier, Ticiana L. Coelho da Silva, Breno M. Freitas, Danielo G. GomesAuthors Info & ClaimsArticle No.: 38, Pages 1 - 8
Abstract
Among the agricultural crops used for human consumption, 75% depends on pollination. As the principal pollinating agent, bees are essential for the food production for humans and the ecosystems sustainability. However, a combination of habitat destruction, climate change and exposure to pesticides and pathogens has led to a significant decrease in bee population. Here we propose a method to recognize status patterns of Apis mellifera colonies through the application of data mining techniques. Using a real dataset from the HiveTool.net containing Apis mellifera temperature, humidity and weight data, we identified 3 status patterns in the observed hive. Our results suggest that the recognized patterns are consistent with a honey bee colony life cycle. Based on the found patterns, we propose a high accuracy classification model capable of automatically identifying colony status for new samples.
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Index Terms
- Data Mining to Characterize Seasonal Patterns of Apis mellifera Honey Bee Colonies
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June 2018
578 pages
ISBN:9781450365598
DOI:10.1145/3229345
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Published: 04 June 2018
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View all- Astuti PHegedűs BOleksa ABagi ZKusza S(2024)Buzzing with Intelligence: Current Issues in Apiculture and the Role of Artificial Intelligence (AI) to Tackle ItInsects10.3390/insects1506041815:6(418)Online publication date: 4-Jun-2024
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