Farm-Level Operational Monitoring in Smart Agriculture: Review and Classification Framework

Modern agriculture faces increasing demands for productivity, sustainability, and real-time operational control, driven by challenges such as input overuse, climate variability, and environmental compliance. Operational monitoring systems have emerged as a critical tool to address these challenges by providing continuous, data-driven insights into field operations like tillage, planting, and spraying. However, the academic and practical understanding of operational monitoring remains fragmented, lacking a unified framework to integrate machine-level sensing, data processing, and decision-making. This paper introduces a classification scheme and conceptual framework for operational monitoring in precision agriculture, aiming to bridge this gap. The framework delineates the data–information flow from data acquisition to the execution of actions resulting from informed decisions, distinguishing between real-time control and strategic analysis. Additionally, the proposed classification categorizes operational monitoring into three functional roles, material accounting, logistics accounting, and predictive maintenance, aligned with the conceptual model of farm ontology. By synthesizing technological advancements in positioning systems, sensors, and data management, this study provides a structured approach for designing and deploying operational monitoring. The findings contribute to systematic thinking in farm information systems, supporting smarter, more responsive agricultural practices. Future research should explore the integration of AI and edge computing to further optimize operational monitoring and decision-making in agriculture.