AFarCloud
About the *project.*
AFarCloud aims to reduce problems and address the economic challenges agriculture faces in productivity and cost-effectiveness. Introducing digital processes across the stages of agricultural production helps address the growing labor shortage, driven in part by rural depopulation.
A core focus of the project is reliable detection, accurate identification, and adequate quantification of pathogens and other factors affecting plant and animal health. This reduces costs, prevents commercial interruptions, and minimizes risks to human health. Advances in technology now enable detection and identification of pathogens and other causes affecting plant and animal health with far greater precision.
Key *objectives.*
Develop Agricultural Farming Drones
Design agriculture-focused drones equipped with multispectral cameras to support crop and livestock monitoring.
Integrate Drones Into the AFarCloud Platform
Connect agricultural drones directly into the AFarCloud platform, enabling coordinated data collection and platform-wide interoperability.
Build Farmer Decision-Support Tools
Create Decision Support System (DSS) tools that help farmers make informed, data-driven decisions.
Generate Sensor Algorithms and Multispectral Maps
Develop algorithms to produce multispectral maps and manage sensor communication, turning raw field data into actionable insights.
Enable Remote Control and Cloud Infrastructure
Create a platform for remote drone control alongside scalable cloud infrastructure (built on Kubernetes) to support the full AFarCloud ecosystem.
Beyond Vision's *role*.
Beyond Vision contributes its drone development expertise to design agriculture-tailored drones fitted with multispectral cameras and to support their integration into the AFarCloud platform. This work underpins the project's remote-control and sensor-data objectives, helping turn aerial data collection into the multispectral maps and decision-support insights farmers rely on.