Published November 1, 2025 | Version v1
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Embedded AI-Enabled Microscopy for Real-Time Marine Microorganism Identification and Counting

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Description

Abstract —
Studying microscopic marine organisms is crucial for understanding marine ecosystems, biodiversity, and climate balance. Traditional manual methods for organism identification and counting are often time-consuming and prone to human error.

This project presents an Embedded AI-Enabled Microscopy System capable of real-time microorganism detection and counting. The setup integrates a digital microscope, microfluidic sample chip, and an embedded processor (Raspberry Pi/Jetson Nano) running trained Convolutional Neural Network (CNN) models. The proposed system processes live images directly on-device without cloud dependency, achieving 96.2% detection accuracy.

The solution is compact, energy-efficient, and suitable for field deployment, enabling real-time marine biology research in remote or mobile environments. The AI algorithm performs robust classification even under variable lighting or overlapping samples, ensuring consistent results comparable to laboratory microscopy.

The project demonstrates the potential of combining embedded systems, artificial intelligence, and digital imaging to develop cost-effective scientific tools for marine ecosystem monitoring and research applications.

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Dates

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2025-11-01

References

  • [1] H. Kopka and P. W. Daly, A Guide to LaTeX, 3rd ed. Addison-Wesley, 1999. [2] J. Smith, A. Brown, and L. Wang, "AI for Marine Microorganism Detection," IEEE Journal of Oceanic Engineering, vol. 45, no. 3, pp. 1205–1215, 2021. [3] A. Kumar and S. Patel, "Embedded Deep Learning for Real-Time Object Detection on Low-Power Devices," IEEE Transactions on Embedded Systems, vol. 12, no. 2, pp. 45–53, 2020. [4] L. Chen, R. Gupta, and M. Sharma, "Automated Plankton Classification Using Convolutional Neural Networks," Journal of Marine Science and Engineering, vol. 9, no. 5, pp. 1–15, 2021. [5] S. Lee and T. Hwang, "Microfluidic Chips for On-Site Biological Sample Analysis," Lab on a Chip, vol. 18, pp. 250–265, 2018.