About the project
Project plans
Development of an AI-supported, decentralized multi-agent drone system for large-scale environmental monitoring using panoramic imaging and time series analysis
Project motivation
In light of the increasing need for precise environmental monitoring, for example in agriculture, forestry or nature conservation, scalable, autonomous sensor networks are becoming increasingly important. Small and medium-sized enterprises in particular need flexible, cost-effective solutions for collecting and analyzing large-scale environmental data. Individual drone deployments often reach their limits in terms of area coverage, analysis automation and interoperability. There is therefore a need for intelligent systems that work efficiently, can be expanded flexibly and can also keep an eye on long-term changes.
Project description
The SmartFarmScape project is developing an innovative drone system that differs significantly from conventional agricultural drones. Instead of relying on individual, centrally controlled drones with high computing power, the project is pursuing a decentralized, scalable multi-agent approach: several low-cost drones act as a coordinated team within a self-organizing mesh network. The organization as a drone formation allows for a comprehensive collection of environmental data - more efficient, more flexible and more robust than previous solutions.
The central innovation is drone-to-drone communication, which allows control and task logic to be distributed dynamically within the network. This not only allows the system to continue working if individual drones fail, but also scales flexibly with the number of devices used. Complex calculations, such as for panorama creation or object recognition using AI, are outsourced to an accompanying edge device on the ground.
The entire process is divided into several stages: The individual images captured by the drones are compiled into high-resolution panoramas using feature-based stitching. A neural network then analyses these panoramas, recognizes trees, for example, records their positions and determines their condition. If such flights are repeated regularly, AI time series analysis can be used to automatically identify changes over time - such as growth, leaf loss or disease.
In contrast to conventional agricultural drones, which usually operate individually and send their data to the cloud for post-processing, SmartFarmScape enables immediate, coordinated and adaptive data collection in real time. The project thus opens up completely new possibilities for cost-efficient, AI-supported environmental monitoring - especially in remote regions or dynamic operational areas without a fixed infrastructure.
Promotion
Funding code
KK5659201FG4