Non-intrusive soil carbon content quantification methods using machine learning algorithms: A comparison of microwave and millimeter wave radar sensors

Abstract

Agricultural and forestry biomass can be converted to biochar through pyrolysis gasification, making it a significant carbon source for soil. Applying biochar to soil is a carbon-negative process that helps combat climate change, sustain soil biodiversity, and regulate water cycling. However, quantifying soil carbon content conventionally is time-consuming, labor-intensive, imprecise, and expensive, making it difficult to accurately measure in-field soil carbon’s effect on storage water and nutrients. To address this challenge, this paper for the first time, reports on extensive lab tests demonstrating non-intrusive methods for sensing soil carbon and related smart biochar applications, such as differentiating between biochar types from various biomass feedstock species, monitoring soil moisture, and biochar water retention capacity using portable microwave and millimeter wave sensors, and machine learning. These methods can be scaled up by deploying the sensor in-field on a mobility platform, either ground or aerial. The paper provides details on the materials, methods, machine learning workflow, and results of our investigations. The significance of this work lays the foundation for assessing carbon-negative technology applications, such as soil carbon content accounting. We validated our quantification method using supervised machine learning algorithms by collecting real soil mixed with known biochar contents in the field. The results show that the millimeter wave sensor achieves high sensing accuracy (up to 100%) with proper classifiers selected and outperforms the microwave sensor by approximately 10%–15% accuracy in sensing soil carbon content.