Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 2861-2869 |
| Number of pages | 9 |
| Journal | IEEE Sensors Journal |
| Volume | 25 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2025 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 2 Zero Hunger
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SDG 8 Decent Work and Economic Growth
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SDG 12 Responsible Consumption and Production
Keywords
- black soldier fly
- event-based sensing
- high-speed counting
- landfill
- neuromorphic vision sensor, food waste
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