Low-cost ai-based multispectral sensor for soil greenhouse gas emission monitoring
An ultra-low-cost 11-band spectral sensor system for field-based detection and quantification of greenhouse gas emissions from soil. Powered by solar-charged batteries and controlled by an Arduino microcontroller, the system uses AI models to analyze spectral data and LoRa wireless networks for data transmission, enabling affordable, scalable environmental monitoring.
Overview
This solution offers a low-cost, field-deployable multispectral sensing system designed to monitor greenhouse gas (GHG) emissions from soil. While laboratory spectral analysis has proven reliable for detecting gaseous chemicals, conventional approaches require costly sample preparation. This innovation adapts proven spectral analysis techniques to an ultra-low-cost 11-band sensor covering the visible and near-infrared (NIR) range, making large-scale, in-field GHG monitoring economically feasible. The system is controlled by an Arduino microcontroller, powered by solar-charged batteries, and transmits data wirelessly via LoRa networks, enabling remote deployment in agricultural and environmental settings.
Technical specifications
Key features:
- Ultra-low-cost 11-band spectral sensor capturing visible and NIR wavelengths for chemical detection
- Arduino-based microcontroller for sensor control and data acquisition
- Solar-charged battery power system supporting long-duration field deployment
- LoRa network integration and wireless data service for remote data transmission
- AI and deep learning models trained to quantify greenhouse gases from spectral signatures
- Spectral feature selection methods adapted from prior plant nutrient stress monitoring research
- Cloud computing compatibility for real-time data processing and analysis
- Potential integration with autonomous mobile platforms (such as robotic field units) for automated field coverage
Technology readiness level
The underlying spectral analysis methodology has been validated through prior research on plant nutrient stress monitoring using hyperspectral imaging in controlled environments. Deep learning models and spectral feature selection techniques developed for industrial hemp nitrogen stress detection have been submitted for patenting, demonstrating proof-of-concept for the analytical approach. The proposed system adapts these validated methods to a lower-cost sensor platform for GHG detection. The research group has also developed battery-powered robotic field platforms with cloud computing capabilities, providing a foundation for autonomous deployment. The system is currently at an early-to-mid development stage, with field validation of GHG detection capabilities using the 11-band sensor still to be completed.
About University of Minnesota
The University of Minnesota is a flagship, comprehensive public research university spanning multiple campuses, with a large research enterprise and clinical integration. Industry engages through co-located labs on the Twin Cities campuses, access to an academic health system for clinical translation, and pilot and field-testing facilities that speed scale-up. A statewide extension network and outreach centers provide real-world sites and data partnerships across Minnesota, while proximity to a dense medtech and Fortune 500 corridor enables frequent collaboration. Research is supported by competitive federal funding, including NIH, NSF, DOE, USDA, and DoD. A dedicated technology transfer office manages IP, licensing, sponsored research agreements, and startup incubation to speed commercialization.
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