Digital Farming

Our Research

Research focus: Digitalization in agriculture

In agriculture, there is a high degree of heterogeneity between the individual actors and the systems used. This makes it difficult to network the systems due to a lack of interoperability. Digital farming solutions (e.g. farm management systems, decision support systems, agricultural machinery) also often meet with low user acceptance due to a lack of transparency.
 
Our research is directed at overcoming the described challenges and focuses on software and systems engineering in the area of digital farming and efficient data management for innovative solutions in the food chain. To this end, the consideration of systems in the entire value chain and a requirements analysis of all different actors is essential.

Our location in Rhineland-Palatinate offers an excellent basis due to the versatile coverage of different areas, such as wine, vegetable and grain cultivation, but also the immediate proximity to research and industry.

Our chair cooperates with the Friends of Digital Farming e.V., in which renowned companies along the value chain, such as BASF, John Deere, Hochwald, Aldi Süd, but also agricultural practitioners, such as the Hofgut Neumühle Teaching and Research Institute, are represented. Furthermore, the chair is active in the national AI lighthouse project Nachhaltige Landwirtschaft mittels KI (Sustainable Agriculture Using AI) and collaborates closely with the Smart Farming program of Fraunhofer IESE in Kaiserslautern.

Current projects

Nachhaltige Landwirtschaft mittels KI (English: Sustainable agriculture using AI) (NaLamKI)

Contact person: Felix Möhrle


01.01.2021 - 31.12.2023

The NaLamKI project follows the goal of promoting digitalization in agriculture. Agriculture is currently facing many challenges, such as the influences of climate change, the increasingly important management of nutrients and plant protection, and the growing shortage of skilled workers. In particular, to ease the burden on farmers, sensor data from ground vehicles will be obtained, merged with data from other sources, and processed using AI methods. This automated data will then be incorporated into an IDSA Agriculture Dataspace compliant with the GAIA-X data infrastructure.

Previous methods of recording soil water conditions, pest infestations, and the detection of foreign objects on agricultural land are mainly done manually by walking on the land or purely on a point-by-point basis using soil probes. A fast, area-wide observation and localization is currently not possible due to the lack of detection mechanisms and the insufficient linking of data. By integrating new measurement methods and fusing them with existing data (soil radar, geology, weather patterns) with the help of AI, soil information should be recorded and presented in a way that can be interpreted by the farmer. The procedure can work similarly for the detection of diseases or the prediction of yield development. To ensure user acceptance, the solutions developed must demonstrably contribute to process optimization and increased sustainability in crop production. To this end, risks, for example due to weather developments or pest infestations, must also be identified at an early stage.

The NaLamKI project aims to achieve the following results:

  • A cloud-based software-as-a-service (SaaS) platform is to be created with open interfaces that are accessible to all players in the ecosystem. The goal of the platform is to provide high-dimensional analysis of aggregated data. By aiming for conformity with the GAIA-X data infrastructure, interoperability and data sovereignty of the different actors shall be ensured.
  • Historical inventory data, soil properties data as well as other sensor data shall be aggregated. This data will serve as a basis for the application of modern AI methods and enable versatile analyses.
  • The automated evaluation of the data obtained is intended to enable versatile optimizations. For example, decision support for farmers should help to achieve a reduction in the application rates for spraying and fertilizing as well as an early detection of pests.

 

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