Carbon Sequestration Strategies in Regenerative Agricultural Systems by Leveraging Wireless Sensor Networks for Precision Carbon Management

¹ Department of computers Techniques engineering, College of technical engineering, The Islamic University, Najaf, Iraq Department of computers Techniques engineering, College of technical engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq Department of computers Techniques engineering, College of technical engineering, The Islamic University of Babylon, Babylon, Iraq
² College of MLT, Ahl Al Bayt University, Karbala, Iraq
³ Department of AIML, GRIET, Hyderabad, Telangana, India

^* Corresponding author: hassanaljawahry@gmail.com

Abstract

Regenerative agricultures systems are vital mechanisms to combat climate change and have the ability to help sequester carbon at a scale that is much larger than any current effort could do. In this study, a total approach towards optimizing carbon sequestration strategies using advanced technologies like Wireless Sensor Network (WSN), Digital Twin model, and predictive algorithms like Random Forest Regression and gradient boosting are presented. It provides the details for the development of a system using WSNs to monitor real-time environmental parameters (soil moisture and temperature and carbon dioxide flux) essential to understanding carbon dynamics. One significant part of this system is the LiCOR LI-8100A system, which allows measuring soil C02 emissions and respiration rates, being precise and continuous monitoring of carbon flux in various practice of regenerative. They include practices of cover cropping, reduced tillage, agroforestry, and organic amendments. Then the digital twin model is fed with the collected data, that is, the soil carbon processes in the real world are mirrored in a virtual platform. It enables dynamic simulations and prediction of the carbon sequestation in different management scenarios. The Random Forest Regression and the Gradient Boosting algorithms are used to analyze the complex interactions such that the variables can be used to thoroughly forecast the most effective carbon sequestration strategies we examined. The predictive models attain greater than 90 percent accuracy of carbon capture efficiency estimation in different regenerative practices, showing a substantial improvement in carbon storage in the soil. For a consistent future carbon sequestration, healthy soil fertility, and reduced greenhouse emissions, a scalable and healthy sustainable agriculture framework of carbon sequestration is presented, which contributes to an agriculture system with better resilience. Furthermore, the project not only provides substantial knowledge on the dynamics of carbon but, more importantly, provides actionable insights that can immediately be used in improving farming practices and support global climate change mitigation.