Integrating autonomous robots and Big Data in precision agriculture: architectures, automation and security challenges for a connected ecosystem

Abstract

Precision agriculture is undergoing a radical transformation driven by the convergence of autonomous robotics and Big Data infrastructures. This article presents a technical and forward-looking analysis of the integration of these technologies as a strategic vector for the future of large-scale agricultural production. The focus is on understanding how digital architectures, distributed systems, and massive data collection can be combined with intelligent robotics to enable more efficient, sustainable, and adaptable operations. The contribution of multidisciplinary experts to this study aims to expand the perspective beyond the agricultural field, integrating insights from areas such as system security, sensor interoperability, applied intelligence in automation, and professional technical training. Within this context, inputs from the defense sector, systems engineering, and tactical education are essential to propose scalable, robust, and secure solutions for the agricultural environment.Based on a review of specialized literature, the discussion explores the role of real-time data infrastructures, the importance of interoperability among platforms, and the potential of predictive algorithms in automated decision-making. The proposal goes beyond merely observing trends, suggesting an evolutionary scenario in which agriculture becomes a fully integrated digital ecosystem, environmentally sensitive, and capable of real-time responsiveness. Finally, technical challenges and research opportunities are identified, with emphasis on scalable architectures, remote area connectivity, and agricultural data security.