Greenhouse Gas Detection using AI Powered Infrared Sensors

The stability of the global climate is seriously threatened by the growing buildup of Greenhouse Gases (GHGs), especially carbon dioxide (CO₂) and methane (CH₄). Because it is colorless and odorless, methane is particularly hard to detect, even though it has a global warming potential that is more than 30 times that of CO₂ over a 100-year period. Traditional detection techniques, like satellite remote sensing, point sensors, and manual inspection, frequently have poor sensitivity, high latency, and limited spatial resolution when it comes to identifying diffuse or low-concentration leaks. This study suggests an AI-powered, multimodal detection framework that combines real-time data processing, nanophotonic sensor improvements, and advanced infrared (IR) spectroscopy in order to overcome these constraints. To increase sensitivity, selectivity, and energy efficiency, the system makes use of the near-infrared (NIR), shortwave infrared (SWIR), and Longwave Infrared (LWIR) bands, which are supported by materials like Metal-Organic Frameworks (MOFs), Carbon Nanotubes (CNTs), and quantum dots. For low-latency performance, embedded FPGA hardware is used for signal processing. Principal Component Analysis (PCA) in conjunction with deep neural networks facilitates the quantification of CO₂ from spectral data, while AI models such as Vision Transformers (e.g., Gasformer) and temporal networks (e.g., GLRNet) are used to segment methane plumes in thermal imagery. Further advancements in system miniaturization and deployability include CMOS-compatible imagers and micro-LED-based illumination sources, enabling compact, low-power, and scalable integration for real-world applications. By combining interpretable, AI-driven analytics with nanophotonic sensor design, this work offers a unified and scalable solution for future GHG monitoring.