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BeeSafe: Detecting Bee Diseases with Multi-Scale Object Detection and Generative Adversarial Networks

CSEF · 2023 Computational Systems & Analysis Honorable_mention Award

Kim Ryan · 10

Overview

Honey bees play a crucial role in our food supply. However, recent studies show that the bee population is declining at an unprecedented rate, disrupting global food supply. The main cause of this decline is deadly parasites, particularly the varroa mite. Varroa mite is the leading cause for mass bee colony collapse. To prevent mass colony collapse, beekeepers regularly monitor varroa mites. However, existing monitoring methods, such as sugar shake and alcohol wash, are labor-intensive, time-consuming, and error-prone. This project proposes a new method, BeeSafe, a mobile app-based bee disease detection system. BeeSafe takes a bee frame image and detects varroa mites in the image in real-time. In its core, BeeSafe proposes a multi-scale object detection technique which uses multiple convolutional neural networks (CNNs). To increase the accuracy of varroa mite detection, CNNs require a large set of bee images with varroa mites, which is very challenging to obtain in practice. To address this shortcoming, BeeSafe leverages the power of generative AI, specifically Generative Adversarial Networks (GAN), to artificially generate datasets for CNN training. We have implemented BeeSafe as a mobile application and ran our inference in Google Cloud using both public datasets. Our performance results show that BeeSafe achieves near real-time bee disease detection (10 second in CPU and 1 second in GPU on average) with high accuracy. With its ability to quickly and accurately detect varroa mites, BeeSafe has a great potential to revolutionize beekeeping practice.

Source coverage

This record comes from a published award list, not a complete project archive. Its abstract comes from CSEF's public project showcase as archived by the Internet Archive before judging (https://web.archive.org/web/20230401224130/https://ca-csef.zfairs.com/showcase/ShowcaseInfo?f=838e60b7-ea75-46e8-865c-fde4864244b3); the version presented may differ.

Awards (1)

  • Category Award: HM

Competition history

  • CSEF 2023 Computational Systems & Analysis · Entry S0818

Resources

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