by Christoph Schmittner (AIT), Christian Hirsch (TU Wien) and Ma Zhendong (AVL List GmbH)
Smart Farming – the application of IoT and Industry4.0 technologies within the agricultural domain – has the potential to increase yield and efficiency while decreasing environmental impact. A European project will address the challenges necessary to reach these goals. The results will be demonstrated in multiple real world agricultural demonstrations.
The agricultural domain is facing new and increased challenges, such as an increasing labor shortage due to the movement from rural areas to cities and the declining attractiveness of agriculture jobs. Simultaneously, there are pressures to increase productivity and cost-effectiveness to supply a rising world population while minimising the use of chemicals and adapting to changing climatic conditions . Future agriculture needs to adopt and expand production processes, technologies and tools – a challenge that will be met through research and development activities from all domains. There are already first approaches: precision farming and automation have already increased productivity and quality while reducing the need for manual labor. The use of high-tech, e.g., digitally-controlled farm implements, and even unmanned aerial vehicles for monitoring and forecasting is also on the rise. Most current solutions address singular tasks. An important aspect for the future is the interoperability and configurability of these systems.
“Smart Farming” applies and combines these technologies with approaches from industry4.0 and smart mobility to address the challenges and develop a holistic system. AFarCloud is an ECSEL JU Project, starting in the second half of 2018 which will enable and extend the usage of smart farming by combining experts from industry4.0, smart mobility and Internet of Things (IoT) with agricultural solution provider and end user.
Figure 1: Example UseCase. An overview of an intended scenario.
IoT sensor networks in the field will detect pests and environmental conditions and a range of other factors affecting plant health. Communicating this information via secured edge nodes with the AfarCloud middleware, and storing them in a backend, enables a fast reaction and adaptation and simultaneously charters and monitors long term trends and compares production and conditions across years. Automated vehicles compensate for the shrinking labor force and enable precision farming by closing the feedback loop between sensing, control and actuation. Automated farming vehicles, working in combination with a distributed sensor network can apply pesticides and fertiliser based on the measured needs of small patches of the complete agricultural production unit. To enable the future of farming, Afarcloud will work on a distributed framework for autonomous farming that will allow the integration and cooperation of agriculture cyber physical systems in real-time in order to increase efficiency, productivity, food quality and reduce farm labor costs. An important aspect of this approach is the holistic view of farming and food production, including production, energy, food quality and services. Taking the complete value chain, from farming equipment to the end-product into account enables a complete approach.
Automated farming vehicles should become usable by farmers and cooperate to enable new applications as basic vehicles can cooperate to address more complex tasks without human interaction. The framework will enable the seamless interaction of multiple devices and systems towards a cooperative system of systems.
A prerequisite is the safety and security of all involved systems . Automated farming vehicles face challenges that only partially overlap with smart mobility, but safe operation and secure communication and cooperation are a necessity for the practical application in both domains. Additionally, sensor networks in the agricultural domain must cope with greater demands on their robustness against environmental and mechanical damages while ensuring the same level of security as sensor networks in less challenging areas. Food security is a critical area to which increased connectivity and automation introduce multiple challenges. There exists the potential for both malicious attackers and human misuse or failures to threaten food supply.
AfarCloud will build upon and extend existing IoT frameworks and standards from multiple domains to develop a holistic, safe and secure agricultural framework . For example, the Data Distribution Service for Real-Time Systems (DDS) standard by OMG will be used to enable reliable, scalable and real-time data exchanges using a publish/subscribe pattern and automotive safety and security standards will be utilised for safety and security of automated farming machines.
AFarCloud outcomes will strengthen the European agricultural domain and support food security by applying ICT-based solutions to farming. Partners throughout the complete value chain, including all key actors needed for the development, demonstration and future market uptake of the precision farming framework are working on the project and supporting a leadership role for Europe in the smart farming market. The results of the project will be demonstrated and evaluated in multiple real-world agricultural demonstration sites, ranging from livestock farming to crop farming.
“AfarCloud is partially funded by the EC Horizon 2020 Programme, ECSEL JU, and the partners National Funding Authorities under grant agreement # 783221”
 G. Feder, A. Willett, and W. Zijp: “Agricultural extension: Generic challenges and the ingredients for solutions,” in Knowledge generation and technical change, Springer, 2001, pp. 313–353.
 C. Schmittner, Z. Ma, and T. Gruber, “Standardization Challenges for Safety and Security of Connected, Automated and Intelligent Vehicles,” presented at the The 3rd International Conference on Connected Vehicles & Expo (ICCVE 2014), Wien, 2014.
 R. Khan, S. U. Khan, R. Zaheer, and S. Khan, “Future Internet: The Internet of Things Architecture, Possible Applications and Key Challenges,” 2012, pp. 257–260.
Christoph Schmittner, Erwin Schoitsch
Austrian Institute of Technology, Austria