Zynq-Based Hardware-Software Codesign Architecture for an Intelligent Hyperspectral Camera

Area of Science:

• * Advanced sensor technology and embedded systems.
• * Real-time data processing and edge computing.
• * Hyperspectral imaging and spectral analysis.

Background:

• * Traditional hyperspectral cameras generate large data cubes, causing bandwidth and storage issues.
• * Impediments to real-time analysis hinder the application of hyperspectral imaging in high-throughput scenarios.
• * Edge-sensing applications require efficient data handling and immediate actionable insights.

Purpose of the Study:

• * To develop an intelligent hyperspectral camera with integrated on-board processing capabilities.
• * To overcome the limitations of traditional hyperspectral data transmission.
• * To enable real-time spectral analysis and information extraction at the edge.

Main Methods:

• * Hardware-software codesign utilizing a Zynq-7035 System on Chip (SoC).
• * Implementation of a row-parallel pipeline in programmable logic (PL) for image acquisition, preprocessing, and spectral matching.
• * Processing System (PS) for command scheduling and a unified DDR3 interface for module decoupling and algorithm integration.

Main Results:

• * Successful execution of Euclidean distance-based spectral matching entirely within the intelligent camera.
• * Achieved a two-orders-of-magnitude reduction in data volume.
• * Accurate identification of diseased regions in leaf samples via push-broom experiments.

Conclusions:

• * The developed intelligent camera architecture effectively enables real-time hyperspectral processing.
• * On-board processing significantly reduces data bottlenecks, facilitating high-throughput edge-sensing tasks.
• * The system demonstrates a viable solution for efficient spectral analysis and actionable data extraction in resource-constrained environments.