Deep learning-based classification models for beehive monitoring
Autor: | Efnan Sora Gunal, Selcan Kaplan Berkaya, Serkan Gunal |
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Rok vydání: | 2021 |
Předmět: |
0106 biological sciences
Beekeeping Computer science Machine learning computer.software_genre 010603 evolutionary biology 01 natural sciences Pollinator Ecology Evolution Behavior and Systematics Beehive Ecology Contextual image classification biology business.industry 010604 marine biology & hydrobiology Applied Mathematics Ecological Modeling Deep learning Honey bee biology.organism_classification Computer Science Applications Computational Theory and Mathematics Modeling and Simulation Varroa Artificial intelligence business Transfer of learning computer |
Zdroj: | Ecological Informatics. 64:101353 |
ISSN: | 1574-9541 |
Popis: | Honey bees are not only the fundamental producers of honey but also the leading pollinators in nature. While honey bees play such a vital role in the ecosystem, they also face a variety of threats, including parasites, ants, hive beetles, and hive robberies, some of which could even lead to the collapse of colonies. Therefore, early and accurate detection of abnormalities at a beehive is crucial to take appropriate countermeasures promptly. In this paper, deep learning-based image classification models are proposed for beehive monitoring. The proposed models particularly classify honey bee images captured at beehives and recognize different conditions, such as healthy bees, pollen-bearing bees, and certain abnormalities, such as Varroa parasites, ant problems, hive robberies, and small hive beetles. The models utilize transfer learning with pre-trained deep neural networks (DNNs) and also a support vector machine classifier with deep features, shallow features, and both deep and shallow features extracted from these DNNs. Three benchmark datasets, consisting of a total of 19,393 honey bee images for different conditions, were used to train and evaluate the models. The results of the extensive experimental work revealed that the proposed models can recognize different conditions as well as abnormalities with an accuracy of up to 99.07% and stand out as good candidates for smart beekeeping and beehive monitoring. |
Databáze: | OpenAIRE |
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