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Automated precision beekeeping for accessing bee brood development and behaviour using deep CNN

Published online by Cambridge University Press:  05 January 2024

Neha Rathore Open the ORCID record for Neha Rathore [Opens in a new window]  and
Dheeraj Agrawal
Neha Rathore*
Affiliation:
Department of Electronics and Communication, Maulana Azad National Institute of Technology (MANIT), Bhopal, India
Dheeraj Agrawal
Affiliation:
Department of Electronics and Communication, Maulana Azad National Institute of Technology (MANIT), Bhopal, India
*
Corresponding author: Neha Rathore; Email: rathore.neha13@gmail.com
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Abstract

Bees play a significant role in the health of terrestrial ecosystems. The decline of bee populations due to colony collapse disorder around the world constitutes a severe ecological danger. Maintaining high yield of honey and understanding of bee behaviour necessitate constant attention to the hives. Research initiatives have been taken to establish monitoring programs to study the behaviour of bees in accessing their habitat. Monitoring the sanitation and development of bee brood allows for preventative measures to be taken against mite infections and an overall improvement in the brood's health. This study proposed a precision beekeeping method that aims to reduce bee colony mortality and improve conventional apiculture through the use of technological tools to gather, analyse, and understand bee colony characteristics. This research presents the application of advanced digital image processing with computer vision techniques for the visual identification and analysis of bee brood at various developing stages. The beehive images are first preprocessed to enhance the important features of object. Further, object is segmented and classified using computer vision techniques. The research is carried out with the images containing variety of immature brood stages. The suggested method and existing methods are tested and compared to evaluate efficiency of proposed methodology.

Keywords

bee brood detectioncell classificationcolony collapse disorderimage processingneural network
Type
Research Paper
Information
Bulletin of Entomological Research , Volume 114 , Issue 1 , February 2024 , pp. 77 - 87
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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