The role of Artificial Intelligence in measuring and modelling soil organic carbon in agricultural lands

Abstract

There is a need to move from today’s disruptive food production systems towards more degenerative agricultural practices. Simultaneously, a worryingly increasing amount of greenhouse gases in the atmosphere incites governments to seek solutions for mitigating the global CO2 burden. Carbon sequestration in agricultural soils is proposed as one of such promising solutions. Despite opposing views on the potential for increased soil carbon to mitigate global warming, the benefits of organic carbon presence in soil and the need for reliable methods for its quantification are widely acknowledged. This thesis provides an overview of some of the innovatory soil carbon measurement techniques as an alternative to labours and expensive traditional approaches and assess the role of Artificial Intelligence in their development. This study consists of both literature review and qualitative research.

The results obtained from the interviews show that novel techniques used to estimate organic carbon content in agricultural soils, such as satellite multispectral and remote sensing hyperspectral imaging, are closely associated with the use of Artificial Intelligence algorithms for real-time decision-making and post-measurement data processing. Private companies are actively utilising Artificial Intelligence for more advanced understanding and monitoring of soil health, whereas environmental organisations involved in soil carbon-related projects only begin to explore these new state-of-art methods. Although their awareness of the latest Artificial Intelligence solutions is somewhat limited, there is a common understanding of when it can be particularly useful.