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.

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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
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article

FlyCount: High-Speed Counting of Black Soldier Flies Using Neuromorphic Sensors

Alexandre Marcireau, Avishkar Seth, Subhas Chandra Mukhopadhyay, Alice James et al.
6 citations
IEEE Sensors Journal
Insect and Arachnid Ecology and Behavior
3.30
article

FlyCount: High-Speed Counting of Black Soldier Flies Using Neuromorphic Sensors

Alexandre Marcireau, Avishkar Seth, Subhas Chandra Mukhopadhyay, Alice James, T. Hu, Ramon Atayde
article en
6 citations

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.

IEEE Sensors JournalVol. 25(2)
University of Technology Sydney (AU), AARNet (Australia) (AU), Western Sydney University (AU), Macquarie University (AU)
Openalex Percentile: Top 7%
Insect and Arachnid Ecology and Behavior
3.30
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