Spatiotemporal Profiling of Aerosol Particulates through Digital Inline Holography: A Synergistic Integration of GPU-Accelerated Reconstructions, Deep Learning Analytics, and Drone-Orchestrated Environmental Field Diagnostics

Nearly 30% of all farmers face an invisible but relentless adversary: the widespread dispersion of aerosol particles like pollen and spores (Hetzel et al., n.d.). For far too long, policymakers and the public have overlooked farmers, overlooking the critical issue of aerosol particle dispersion. Stemming from the COVID-19 pandemic, this issue affects agriculture, public health, and the environment. These particles contribute to respiratory problems, reduced agricultural efficiency, and ecological impacts, yet current diagnostic tools often lack the precision to identify these aerosols (Singh & Kumar, 2022). This paper discusses the integration of Digital Inline Holography (DIH), which offers an innovative method to address these concerns. It has the potential to transform the understanding of aerosol behavior. Current techniques like laser diffraction have limitations when it comes to studying aerosols. So, can DIH actually enable more precise and versatile aerosol characterization? Yes, DIH actually provides sharper imaging and faster analysis and is more cost-effective, making it a better tool for understanding particle behavior in multiple settings. This project is designed to develop a novel DIH system to analyze aerosols with unprecedented precision. It extracts detailed 3D particle information through just four key steps: recording, 3D reconstruction, particle segmentation, and 3D tracking. This system uses a laser-based holographic setup, GPU-powered processing units, and pattern recognition algorithms to integrate holography and machine learning algorithms for particle data processing. The project will also go over DIH’s potential in drone-based aerosol sampling. The results indicate that DIH, integrated with other innovations such as drone technology, successfully mapped aerosol concentrations and particle size distributions in multiple settings, identifying particles ranging from approximately 10 to 1,000 micrometers in diameter. Additionally, DIH’s real-time capabilities provide an efficient method for large-scale environmental monitoring, catching aerosol oscillations that traditional methods often miss. This novel approach allows for better predictive modeling of airborne contaminants, aiding in pollution control and agricultural disease prevention. The results indicate that DIH can accurately monitor airborne contaminants, presenting meaningful refinements in agricultural efficiency and public health medications. This research highlights DIH’s capability to address real-world issues by improving air quality monitoring, tracking respiratory aerosols for disease prevention, and studying spore and pollen spread in agrarian settings. By combining DIH with machine learning and automation, this study paves the way for future breakthroughs in aerosol characterization, ensuring more effective environmental management and safeguarding public health.