Deep learning-based classification models for beehive monitoring

Kaplan Berkaya S., ŞORA GÜNAL E., Gunal S.

Ecological Informatics, vol.64, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 64
  • Publication Date: 2021
  • Doi Number: 10.1016/j.ecoinf.2021.101353
  • Journal Name: Ecological Informatics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, BIOSIS, CAB Abstracts, Geobase, Pollution Abstracts, Veterinary Science Database
  • Keywords: Beehive monitoring, Deep learning, Honey bee, Smart agriculture, Varroa detection, IMAGE CLASSIFICATION, VARROA-DESTRUCTOR, NEURAL-NETWORKS, PARASITE
  • Eskisehir Osmangazi University Affiliated: Yes


© 2021 Elsevier B.V.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.