Abstract

Farming is facing many economic challenges in terms of productivity and cost-effectiveness. Labor shortage partly due to depopulation of rural areas, especially in Europe, is another challenge. Domain specific problems such as accurate monitoring of soil and crop properties and animal health are key factors for minimizing economical risks, and not risking human health. The ECSEL AFarCloud (Aggregate Farming in the Cloud) project will provide a distributed platform for autonomous farming that will allow the integration and cooperation of agriculture Cyber Physical Systems in real-time in order to increase efficiency, productivity, animal health, food quality and reduce farm labor costs. Moreover, such a platform can be integrated with farm management software to support monitoring and decision-making solutions based on big data and real-time data mining techniques.

Highlights

  • Our societies are facing tremendous challenges in order to build a sustainable future across different regions of the globe

  • By integrating the different technologies and services, such as software components, legacy vehicles, modern Unmanned Ground Vehicles (UGV), and Unmanned Aerial Vehicles (UAV), and other equipment through a middleware so­ lution with a common information model and application interface this project aims at meet the challenges of future farming as described above

  • The adaptive autonomy framework will help the design of highly com­ plex intelligent robots/vehicles that can adapt to the service needs of the operators, through the ability to adapt themselves to the operator in a way which is not possible today

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Summary

Introduction

Our societies are facing tremendous challenges in order to build a sustainable future across different regions of the globe. (semi-) autonomous solutions for (i) grafting to seeding and planting, (ii) monitoring and harvesting to sorting, (iii) packaging and boxing, and (iv) livestock management and animal welfare. The drawbacks of these approaches are that, firstly, they are calibrated only for a specific task, without the ability to be integrated in a more complex service to provide a holistic solution. Note that, farming adds nitrogen and phosphorus to terrestrial ecosystems [2, 3] These chemicals present an occupational threat extending to farm workers, their families and potentially to inhabitants of areas exposed to their application on crops, on vineyards etc. We highlight project goals, explain selected approach, describe application domains, and discuss main implementation issues

The AFarCloud solution
Beyond the state of art - AFarCloud Objectives
Cloud computing and monitoring services
Adaptive autonomous multi-agent system and decision-support systems
Guarantee reliability in communications between cyber-physical systems
STO 1: Semantic middleware and shared information model
STO2: Intelligent coordination and decision-making solutions
STO 3: Environment characterization platform
STO 4: On the move sensors and actuators
Validation scenarios
AFarCloud cropping management scenarios
AFarCloud livestock management scenarios
Contributions and achievements
AFarCloud platform architecture
The Farm management system
Environment characterization platform
Sensors and actuators development
How to realize autonomy
Semi-autonomous vehicles
Findings
Conclusions

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