Smart Agriculture: Integration of IoT, AI and Big Data in Farm Management

Abstract

This paper discusses a crucial component of contemporary agriculture: using Big Data, Artificial Intelligence (AI), and the Internet of Things (IoT) to monitor and reduce carbon emissions from agriculture. We concentrate on how these cutting-edge technologies augment Climate-Smart Agriculture (CSA) and advance sustainable farming methods. A thorough analysis of the ways in which IoT, Big Data, and AI can be integrated to track carbon footprints and promote more general sustainability goals in agriculture is given in this research. As a significant contribution, we provide a practical, all-encompassing system architecture that integrates real-time data analytics, predictive modeling, and IoT-enabled sensors created especially for carbon footprint assessment. This paper assesses how well these technologies work to enhance emission monitoring, operational effectiveness, and environmental compliance while highlighting their observable advantages through real-world case studies. The rigorous analysis of issues including data interoperability, device energy efficiency, and implementation costs also sheds light on current research gaps. The report also outlines potential future paths, such as blockchain-based carbon credit trading platforms, scalable IoT-based carbon markets, and machine learning (ML) algorithms for precision farming. The goal is to offer helpful guidance on how to apply cutting-edge technology in the agricultural sector to achieve carbon neutrality and environmental sustainability.