With the expansion of digital infrastructure, these technologies will create new opportunities for food production of the future. After many innovation cycles in agriculture such as through mechanization, pesticide development, mineral fertilizers, plant cultivation and precision farming, digitization is cred- ited with the potential for the next innovation cycle. It is expected that by adapting and further developing the technology with the help of digitization, new optimization potential in agriculture can be identified and used 8 . This includes automation using drones and robots. If you believe forecasts by ex- perts, many swarm-based robots will do the field work of tomorrow in the future, which will further relieve farmers. An overall and sustainable change in agriculture will therefore be promoted. Automation is expected to reduce the size of future machines and tasks will be distributed 9/10/11 . However, for the practical use of these machines and robots, the necessary technology and infrastructure must first be available. The success of digiti- zation depends heavily on the actuators used. Because only if information gained through digitization can be suitably converted into activities and actions can a benefit be generated for the farmer. Currently, the increase in the resulting data is exponential. Artificial Intelligence (AI) applications require a doubled computing requirement every 3.5 months on average 12 . This also increases the costs for data analysis, data management and the necessary digital infrastructure. This generates a lot of data that is never used to increase productivity. Even if intelligent algorithms generate added value from the data, this knowledge must be put into practice. This is only possible with suitable automation of the processes. Therefore, the success of digitization and AI is directly related to robotics and automation. New information obtained through digitization should be automated, accurate, energy-efficient and user-friendly in actions and recommendations for ac- tion can be transferred. Therefore, the requirement arises for independently operating robots and machines to take as many decision steps as possible for the user, or at least to support the user in doing so. The mechanics must be able to convert the finely resolved data and information practically in order to be able to control sections or even perform an individual plant pro- cessing. The future lies in the direct, local application of sensor data, which is supported by cloud-based information. 8 BMEL, “Digitalisierung in der Landwirtschaft Chancen nutzen - Risiken minimieren”, 2019. 9 S. M. Pedersen, S. Fountas, H. Have and B. S. Blackmore, “Agricultural robots - system analysis and economic feasibility” Precis. Agric., vol. 7, no. 4, pp. 295–308, Jul. 2006. 10 3 B S. Blackmore, HW Griepentrog and S. Fountas, “Autonomous Systems for European Agriculture” in Automation Technology for Off-Road Equipment, 2006. 11 T Duckett et al., “Agricultural Robotics: The Future of Robotic Agriculture” UK-RAS White Pap., pp. 36, 2018 12 F Müssig, “AMD- CEO Lisa Su schaut in die Halbleiter-Zukunft” heise online, 2019. [Online]. Available: https://www.heise.de/newsticker/meldung/AMD-CEO-Lisa-Su-schaut-in-die-Halbleiter-Zukunft-4500699.html. [Accessed: 04-Sep2019] 5
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