Precision Regenerative Farming: Using AI to Optimize Soil Carbon Sequestration

Evaluating the Synergy Between Machine Learning and Regenerative Soil Management

Abstract

As the agricultural sector seeks to mitigate the impacts of climate change, the integration of Artificial Intelligence (AI) into regenerative farming practices offers a promising path toward high-efficiency carbon sequestration. This article examines how machine learning algorithms can analyze multi-spectral satellite imagery and real-time soil sensor data to optimize cover cropping and no-till practices. The research presents a framework for "Dynamic Carbon Accounting," allowing farmers to quantify sequestration rates with unprecedented precision. By comparing AI-driven plots against traditional regenerative methods, the study demonstrates a significant increase in soil organic matter (SOM) and moisture retention. This inaugural article for JARS highlights the role of digital agriculture in achieving global net-zero targets while ensuring long-term soil fertility.