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Identifying Queenlessness in Honeybee Hives from Audio Signals Using Machine Learning

التفاصيل البيبلوغرافية
العنوان: Identifying Queenlessness in Honeybee Hives from Audio Signals Using Machine Learning
المؤلفون: Stenford Ruvinga, Gordon Hunter, Olga Duran, Jean-Christophe Nebel
المصدر: Electronics, Vol 12, Iss 7, p 1627 (2023)
بيانات النشر: MDPI AG
سنة النشر: 2023
المجموعة: Directory of Open Access Journals: DOAJ Articles
مصطلحات موضوعية: honeybees, queen bee, bee colony, audio signal, CNN, LSTM, Electronics, TK7800-8360
الوصف: Honeybees are vital to both the agricultural industry and the wider ecological system, most importantly for their role as major pollinators of flowering plants, many of which are food crops. Honeybee colonies are dependent on having a healthy queen for their long-term survival since the queen bee is the only reproductive female in the colony. Thus, as the death or loss of the queen is of great negative impact for the well-being of a honeybee colony, beekeepers need to be aware if a queen has died in any of their hives so that appropriate remedial action can be taken. In this paper, we describe our approaches to using acoustic signals recorded in beehives and machine learning algorithms to identify whether beehives do or do not contain a healthy queen. Our results are extremely positive and should help beekeepers decide whether intervention is needed to preserve the colony in each of their hives.
نوع الوثيقة: article in journal/newspaper
اللغة: English
تدمد: 2079-9292
العلاقة: https://www.mdpi.com/2079-9292/12/7/1627Test; https://doaj.org/toc/2079-9292Test; https://doaj.org/article/84d4456ac6da4cb8bf6801ad763fb966Test
DOI: 10.3390/electronics12071627
الإتاحة: https://doi.org/10.3390/electronics12071627Test
https://doaj.org/article/84d4456ac6da4cb8bf6801ad763fb966Test
رقم الانضمام: edsbas.73353C7
قاعدة البيانات: BASE
الوصف
تدمد:20799292
DOI:10.3390/electronics12071627