The Role of Artificial Intelligence in Modern Agricultural Management
Keywords:
Artificial Intelligence, Agricultural Management, Socio-Technical Systems, Precision Agriculture, Data Governance, Sustainable Infrastructure, Autonomous Systems.Abstract
The global agricultural landscape is currently navigating a profound transformation driven by the convergence of climate instability, resource scarcity, and the integration of advanced computational intelligence. This paper examines the role of Artificial Intelligence (AI) in modern agricultural management, moving beyond functional applications to explore the system-level architectures, socio-technical trade-offs, and governance frameworks necessary for its large-scale deployment. As agricultural management shifts from manual, experience-based decision-making to data-driven, autonomous systems, the complexity of managing these cyber-physical infrastructures increases exponentially. We analyze the architectural requirements of AI-integrated farming, ranging from edge-based sensor fusion to cloud-driven predictive modeling, while addressing critical concerns regarding system robustness, data sovereignty, and the digital divide in rural communities. The study further investigates the policy implications of AI adoption, arguing that technical efficiency must be balanced with considerations of fairness and long-term sustainability. By synthesizing perspectives from engineering, agronomy, and social science, this research provides a comprehensive overview of how AI reconfigures the agricultural sector, offering insights into the structural and ethical challenges that must be addressed to ensure a resilient and equitable global food system.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
