Bayesian Ordinal Regression for Crop Development and Disease Assessment

The ICOS ecosystem in smart agriculture

In modern agriculture, the seamless flow of data from edge devices to cloud-based platforms is fundamental to maximizing the efficiency and effectiveness of robotic and technological interventions. Distributed processing, data transfer, connectivity, and security are integral components of this data-driven approach. At the edge of the agricultural system, sensors, drones, and robotic devices collect vast amounts of data on soil health, crop conditions, weather patterns, and more. These edge devices often operate in remote areas with limited connectivity, necessitating onboard processing capabilities to analyze data in real-time and make immediate decisions. Edge computing minimizes latency and bandwidth requirements by processing data locally, enabling rapid response to changing conditions without relying on constant communication with centralized servers. However local processes are limited by device computational power.