Comparative Study of Machine Learning Models for Bee Colony Acoustic Pattern Classification on Low Computational Resources Article Swipe
Antonio Robles-Guerrero
,
Tonatiuh Saucedo-Anaya
,
Carlos Guerrero-Méndez
,
Salvador Gómez-Jiménez
,
David Navarro-Solís
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23010460
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23010460
In precision beekeeping, the automatic recognition of colony states to assess the health status of bee colonies with dedicated hardware is an important challenge for researchers, and the use of machine learning (ML) models to predict acoustic patterns has increased attention. In this work, five classification ML algorithms were compared to find a model with the best performance and the lowest computational cost for identifying colony states by analyzing acoustic patterns. Several metrics were computed to evaluate the performance of the models, and the code execution time was measured (in the training and testing process) as a CPU usage measure. Furthermore, a simple and efficient methodology for dataset prepossessing is presented; this allows the possibility to train and test the models in very short times on limited resources hardware, such as the Raspberry Pi computer, moreover, achieving a high classification performance (above 95%) in all the ML models. The aim is to reduce power consumption and improves the battery life on a monitor system for automatic recognition of bee colony states.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23010460
- https://www.mdpi.com/1424-8220/23/1/460/pdf?version=1672904133
- OA Status
- gold
- Cited By
- 16
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313471457
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4313471457Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s23010460Digital Object Identifier
- Title
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Comparative Study of Machine Learning Models for Bee Colony Acoustic Pattern Classification on Low Computational ResourcesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
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Antonio Robles-Guerrero, Tonatiuh Saucedo-Anaya, Carlos Guerrero-Méndez, Salvador Gómez-Jiménez, David Navarro-SolísList of authors in order
- Landing page
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https://doi.org/10.3390/s23010460Publisher landing page
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https://www.mdpi.com/1424-8220/23/1/460/pdf?version=1672904133Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/23/1/460/pdf?version=1672904133Direct OA link when available
- Concepts
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Computer science, Process (computing), Machine learning, Artificial intelligence, Beekeeping, Power consumption, Code (set theory), Source code, Power (physics), Operating system, Biology, Set (abstract data type), Programming language, Physics, Quantum mechanics, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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16Total citation count in OpenAlex
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2025: 6, 2024: 6, 2023: 4Per-year citation counts (last 5 years)
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55Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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