CONTINUE TO GROW.
The use of artificial intelligence in vertical and precision farming makes farming more efficient and ensures optimal results.
50% of our sites are located in regions that are intensively influenced by the agricultural sector. Therefore, we are strongly connected to the industry and have been supporting companies in the agricultural and agricultural machinery sector for over 20 years.
To ensure that we can continue to provide you with the best possible advice and support in the future, we are a member of several research consortia in the agricultural sector and are therefore always up to date in order to optimize your value-added chain in the long term together with you.
Since digitization has also found its way into agriculture, the industry is undergoing rapid change. This has created completely new challenges for the associated companies. Complex data management and networked exchange with other companies is one of the key factors for long-term competitiveness.
The Internet of Things (IoT), cloud computing, big data analysis using AI and autonomous machines are already being used more and more and will become increasingly important in the future to meet the growing demand for food and the greater fluctuations in environmental conditions.
Precision? Digital? Smart?
What is the difference in farming?
The increasing use and benefits of modern technologies in agriculture are omnipresent. Modern technologies have become an important part of the business for every player in the industry. These modern technologies are used to increase efficiency and reduce the costs.
The development of digital technology and the modernization of agriculture have produced new concepts and terminology such as precision farming, digital farming and smart farming. These terms are often used synonymously, but they mean different things.
Optimization of agricultural processes by using software and hardware
Precision Farming can be understood as encompassing everything that makes agricultural practice more precise, optimized and controlled in relation to economic plants and stock farming. An important component is the use of information technology and a wide range of things such as
- autonomous vehicles
- GPS steering
- soil samples and
The essential point here is the optimization. For example: Instead of applying the same amount of fertilizers everywhere on a field, the soil fluctuations and individual soil conditions within the field are measured and the fertilizer strategy is adjusted accordingly. This leads to an optimized usage of fertilizer which saves costs and reduces the environmental impact.
The principles of precision farming have been around for more than 25 years, but only in the last 10 years, they became clearly established. This is mainly due to technology progress. The introduction of mobile devices, the access to high-speed-internet, the cost-effective and reliable satellite-communication, e.g. for positioning and picture recording, and the specific agriculture machines are important technologies that characterize this trend.
Added value from the generated data using AI and big data
The character of digital farming lies primarily in the added value that is obtained from the data. This is not just about the existence and the availability of data. Rather the aim is to develop usable information and make a meaningful added value from this data. This is often the domain of artificial intelligence, in which reliable predictions are to be generated by adding the collected data. This ranges from the expected profitability of a field to the perfect fertilization strategy, taking into account weather, soil conditions and climatic forecasts.
All this offers the opportunity to increase production in the agricultural sector, save costs in the long term, eliminate risks and guarantee sustainability.
Optimized access to generated data using software & sensors
Basically, smart farming is about the use and application of information and data technologies to optimize complex agricultural systems and processes.
The focus is on access to data and how the information collected can be used intelligently. The goal is thus to increase the quality and quantity of the products and at the same time to optimize the labor production. Or, in simpler terms: to produce more foods with less investments and the same amount of land.
The technologies used in smart agriculture range from IoT and robotics to drones and AI. Using these tools, for example, field conditions can be monitored without having to enter the field.
The entire process of smart farming is controlled by software while being monitored by sensors. Through a variety of optimizations, this enables prices to be reduced, yields to be increased, and quality and availability to be improved.
Determine yourself who, for how long, with whom, what data, and to what extent!
As implementation and certification partner of DKE-Data GmbH & Co KG, we have created a solution for this with the Agrirouter.
As a neutral entity, the data exchange platform solves a core problem in the digitization of agriculture: it enables the exchange of data between machine and agricultural software applications from different manufacturers. Even existing machines can be easily connected to an Agrirouter account via telemetry connections available on the market.
As a user, you can create your own personal Agrirouter account free of charge and put it together individually. Only you define the routes (data paths) in the Settings Center on which your data is transported.
We certify your application.
Use the advantages of German data management with the Agrirouter right now. We are "Trusted Agrirouter Certification Partner" for the certification of applications that want to communicate with the agrirouter. In addition, we give you, with our holistic quality management, suggestions for improvement for your application. If required, we also support you in (further) development and provide you with our second level support.
We love exchange and knowledge transfer. Our experts have published their knowledge and experiences from practice:
Barenkamp, M. (2021). Why the earth is getting a digital twin.
In: Wirtschaftsinformatik & Management.
Barenkamp, M. (2020). A New IoT Gateway for Artificial Intelligence in Agriculture.
In: 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, pp. 1–5.
Barenkamp, M. (2020): IoT Security Best Practices. Presentation of a Security and Authentication Concept in the Realization of (Industrial) Internet of Things (IIoT) Applications - A Case Study on Client Side Authentication in IoT.
In: HMD Praxis der Wirtschaftsinformatik.
Barenkamp, M. & Niemöller, D. (2020). ARchitecture — Insights From Theory and Practice.
In: 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, pp. 1–6.
Barenkamp, M., Schoenke, J., Zarvic, N. & Thomas, O. (2019). IoT Best Practices. Identify and address pitfalls in the realization of (Industrial) Internet of Things (IIoT) projects at an early stage.
In: HMD, 56, pp. 1157–1177.
Metzger, D., Niemöller, C. & Thomas, O. (2017). Design and demonstration of an engineering method for service support systems.
In: Information Systems and e-Business Management, 15, pp. 789–823.
Niemöller, C., Özcan, D., Metzger, D. & Thomas, O. (2014). Towards a Design Science-Driven Product-Service System Engineering Methodology.
In: M. C. Tremblay, D. VanderMeer, M. Rothenberger, A. Gupta & V. Yoon (Ed.). Design Science Research in Information Systems and Technologies, LNCS 8463, Miami: Springer, Cham, pp. 180–193.