Distributed sensor network for agricultural greenhouse gas flux detection
Machine learning-powered sensor network that accurately measures agricultural greenhouse gas fluxes. Combines distributed gas concentration sensors with eddy covariance flux tower training data to detect and attribute CO2 and nitrous oxide emissions in real-world farming environments.
Overview
This solution addresses one of agriculture's hardest measurement challenges: quantifying greenhouse gas fluxes accurately, affordably, and at scale. By pairing distributed gas concentration sensor arrays with eddy covariance flux tower data and applying machine learning data fusion techniques, the platform delivers reliable measurements of agricultural greenhouse gas emissions, including CO2 spikes and nitrous oxide flux. The approach replaces expensive, stationary research-grade instrumentation with a scalable network of rugged, affordable ground-truth sensors, making field-level emissions monitoring practical for working farms.
Technical specifications
Core capabilities:
- Distributed gas concentration sensor arrays deployed across agricultural plots, including vegetable and vineyard systems
- Soil carbon respiration sensors already commercially launched, with nitrous oxide sensors in preparation
- Machine learning data fusion models trained on colocated eddy covariance flux tower data to improve accuracy
- Detection and attribution of CO2 emission spikes tied to specific field events and seasonal patterns
- Modular sensor architecture designed for rugged field deployment and scalable production
- Integration with a predictive agriculture platform that delivers insights through a mobile app and web-based grower portal
How it works: Sensor arrays are colocated with eddy covariance flux towers. The flux towers provide high-accuracy training data, while the sensor arrays generate production-scale data. Machine learning models trained on this paired dataset estimate greenhouse gas fluxes across broader areas and additional locations, with accuracy evaluated for seasonality and environmental variability.
Technology readiness level
The soil carbon respiration sensor is commercially launched and actively deployed. Nitrous oxide sensors are in preparation. The platform is currently being used to detect and attribute CO2 emission spikes in active vegetable and vineyard agricultural plots. Next-stage validation includes deploying production and prototype sensor arrays alongside eddy covariance flux towers, training and evaluating machine learning models for seasonal accuracy across multiple locations, and refining prototype sensor design for scaled production. The technology has been recognized by the National Science Foundation and the USDA.
About Agrolog.Ag
Agrology is a climate technology company that provides a predictive agriculture platform designed to help growers manage climate volatility, soil health, and resource efficiency. The system integrates rugged, affordable GroundTruth array sensors with advanced machine learning models to capture real-time, ground-truth data from the field. This platform monitors parameters including soil respiration, microbial biomass, soil moisture, canopy temperature, and carbon flux. By processing this localized data, Agrology offers predictive insights for threats such as extreme weather, irrigation needs, pest outbreaks, and wildfire smoke taint. The technology is modular and designed to be scalable, offering farmers and agricultural organizations actionable alerts via a mobile app and web-based grower portal without the need for manual sampling or lab results.
The company focuses on empowering farmers to implement regenerative practices, improve crop yields, reduce input costs, and quantify sustainability outcomes for certification or carbon markets. Trusted by various specialty farms and wineries, Agrology’s platform serves as a tool for economic viability and environmental stewardship, helping stakeholders adapt to climate change. Founded in 2019 and structured as a Public Benefit Corporation, Agrology has received support and recognition from organizations such as the National Science Foundation, the USDA, and leading academic partners. Through its data-driven approach, the company aims to move agriculture toward more profitable and resilient systems, providing evidence-based intelligence that allows for proactive field management.
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