Acoustic Analysis of Beehives for Precision Beekeeping Based on the Internet of Things: A Case Study on Improving Hive Health and Productivity

Articles in Press

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Agricultural Machinery Engineering, Agriculture and Natural Resources Campus, University of Tehran, Tehran, Iran

2 Professor, Department of Agricultural Machinery Engineering, Agriculture and Natural Resources Campus, University of Tehran, Tehran, Iran

3 Associate Professor, Department of Mechanical engineering of biosystems, Razi University, Kermanshah, Iran

Abstract

Pollination is essential for the sexual reproduction of many crops, fruits, and most wild plants. Among animal pollinators, solitary and social bees play a major role. In addition to their role in pollinating wild plants, managed honeybee colonies are economically the most valuable group of pollinators for monoculture crops and fruits worldwide. This study presents a novel method for diagnosing honeybee colony diseases and problems using sound analysis and deep learning. First, the sounds produced by the honeybee colony were recorded by designing a smart beehive and placing a microphone in an optimal location. Then, by converting the audio signals into spectrograms and using convolutional neural networks, sound patterns associated with various diseases and problems such as queenless ness, varroa mite infestation, and foulbrood and nosema diseases were identified. The results showed that this method is capable of diagnosing these problems with an accuracy of over 98%. For example, the model was able to detect queenlessness with 98.62% accuracy, the probability of varroa mite presence with 98.59% accuracy, and the probability of foulbrood disease with 98.71% accuracy. Finally, by implementing the Internet of Things in the hive management system, a significant improvement in the quantity and quality of honey produced was observed. This research shows that sound analysis and deep learning can be used as a powerful tool for monitoring the health of honeybee colonies and increasing productivity in the beekeeping industry.

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Iranian Journal of Biosystems Engineering

Articles in Press, Accepted Manuscript
Available Online from 03 February 2025

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History

  • Receive Date: 05 October 2024
  • Revise Date: 30 January 2025
  • Accept Date: 03 February 2025

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