FlyCount: High-Speed Counting of Black Soldier Flies Using Neuromorphic Sensors
Black soldier fly larvae (BSFL) play a vital role in waste management by efficiently converting organic waste into protein-rich feed, thereby reducing environmental impact and supporting sustainable agriculture. To optimize BSFL production, precise monitoring of adult flies is essential. This article introduces FlyCount, a system developed with neuromorphic vision sensors and a custom spike detection algorithm for real-time, accurate fly counting. The system’s architecture integrates advanced event stream processing with dynamic thresholding, achieving 95% accuracy across 30 trials, validated against manual counts. FlyCount also visualizes fly trails, providing real-time insights into movement patterns. Moreover, variations in count plots enable monitoring of peak fly departures from the hatch, offering critical data for optimizing hatching and collection processes. This data-driven approach enhances the scalability of waste processing systems, fostering sustainability and a brighter future, while also representing a foundational step toward a broader goal of leveraging neuromorphic systems to tackle the complex challenges facing humanity.
Authors
- Alexandre Marcireau (ORCID: https://orcid.org/0000-0003-4488-033X)
- Avishkar Seth (ORCID: https://orcid.org/0000-0002-0902-9753)
- Subhas Chandra Mukhopadhyay (ORCID: https://orcid.org/0000-0002-8600-5907)
- Alice James (ORCID: https://orcid.org/0000-0002-5997-8471)
- T. Hu
- Ramon Atayde
Institutions
- University of Technology Sydney (AU)
- AARNet (Australia) (AU)
- Western Sydney University (AU)
- Macquarie University (AU)
Publication Details
- Journal
- IEEE Sensors Journal
- Published
- 2024-11-27
- DOI
- https://doi.org/10.1109/jsen.2024.3504289
- Citations
- 6
- Primary Topic
- Insect and Arachnid Ecology and Behavior
- Type
- article
- Field-Weighted Citation Impact
- 3.30