Work

Monitoring IoT and Robotics Data for Sustainable Agricultural Practices Using a New Edge–Fog–Cloud Architecture

Year: 2026

Type: article

Abstract: Modern agricultural operations generate high-volume and diverse data (historical and stream) from various sources, including IoT devices, robots, and drones. This paper presents a novel smart farming architecture specifically designed to efficiently manage and process this complex data landscape.The proposed architecture comprises five distinct, interconnected layers: The Source Layer, the Ingestion Layer, the Batch Layer, the Speed Layer, and the Governance Layer. The Source Layer serves as the unified entry point, accommodating structured, spatial, and image data from sensors, Drones, and ROS-equipped robots. The Ingestion Layer uses a hybrid fog/cloud architecture with Kafka for real-time streams and for batch processing of historical data. Data is then segregated for processing: The cloud-deployed Batch Layer employs a Hadoop cluster, Spark, Hive, and Drill for large-scale historical analysis, while the Speed Layer utilizes Geoflink and PostGIS for low-latency, real-time geovisualization. Finally, the Governance Layer guarantees data quality, lineage, and organization across all components using Open Metadata. This layered, hybrid approach provides a scalable and resilient framework capable of transforming raw agricultural data into timely, actionable insights, addressing the critical need for advanced data management in smart farming.

Source: Computers

DOI: 10.3390/computers15010032

Authors: Mohamed El-Ouati, Sandro Bimonte, Nicolas Tricot

Institutions: Université Clermont Auvergne, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Industry, innovation and infrastructure, Zero hunger

Open Access status: gold

Estimated APC paid: $2,172