Page 19 - Digital Agriculture: A Standards Snapshot

Digital Agriculture: A Standards Snapshot

FG-AI4A: Technologies in Focus

The FG-AI4A explored the integration of a variety of technologies including AI and IoT
technologies to optimize farming practices, enhance crop management, and improve resource
utilization and sustainability. It analyzed several successful use cases for the formulation of best
practices as contained in its Use-case Report. Key examples include the use of Cosmic-Ray
Neutron Sensing (CRNS) for soil moisture monitoring, AI-powered pest and disease monitoring
in viticulture and tea plantations, and autonomous irrigation systems and cultivation systems. The
report also emphasizes best practices such as real-time monitoring, continuous data analysis,
thorough field testing, and sustainability through optimized resource use.

Figure 12: Snapshot of technologies in Use-case Report

Recognizing the crucial role of data modeling in digital agriculture, FG-AI4A also produced
a Report detailing the use of sensors, drones, and other smart farming equipment to gather
real-time data, which is then analyzed with AI algorithms for precision agriculture. The Report
highlights the significance of semantic data models for standardizing data representation
and tackles challenges such as data quality, volume, integration, accessibility, privacy, and
environmental variability. It also outlines the necessary steps for implementing AI-based
modeling in agriculture.

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