Research for an environmentally controlled, diversified crop production
We develop technologies and processes for agricultural crop production. In the bioeconomy, plant biomass not only provides food and feed, but is also the basis for biobased materials and energy. With our research, we follow the concept of an environmentally controlled, highly diversified agroecological production. In doing so, we take advantage of the Digital Transformation and support the transition from precision agriculture to an "Diversity by Precision" approach.
Biodiversity loss is currently one of the most pressing problems worldwide. The mixed cultivation of different crops on the same area, i.e. the interaction of a wide variety of plants, animals and microorganisms in so-called inter- or mixed cropping systems, has many advantages, including improved resilience and higher overall yields.
The agile management of such diverse and complex production systems requires adaptation of machinery, sensors, control systems, algorithms, and especially of management strategies. The systemic approach to circular farming also requires a better understanding of the microbiome and its interactions within agricultural systems.
Our research addresses the development of smart sensors for the digital assessment of environmental conditions and plant status as well as methodological issues of intelligent image analysis. These digital methods are expected to contribute decisively to advancing sensor-based process control and automation as a prerequisite for the complex control systems of a circular bioeconomy.
A central task is the development of digital twins for knowledge-based, precise, dynamic and adaptive process control of these very complex systems.
For our research, we benefit from the ATB Fieldlab for Digital Agriculture in Marquardt with a test track for soil sensors and an installation for automated data acquisition in fruit tree cultures. In addition, research work will also be implemented in the Leibniz Innovation Farm at Groß Kreutz (currently under construction).
Healthy soils
The research focus is on the further development of proximal soil sensor technology in order to be able to digitally record all information relevant for sustainable crop production, and on the development and application of biological sensor systems for analyzing soil health. Our objective is the sustainable use and improvement of the soil.
Plant health
Our research focus is on a precise plant monitoring by using especially optical sensor systems like LIDAR or multispectral cameras - both in agriculture and in horticulture. Data from in-situ, proximity and remote sensing provide information on the status of plants as well as pathogens or pests. The aim of a precise plant monitoring is to design plant protection in an environmentally friendly way and to secure the food supply by strengthening plant health.
Automation
Digitization and robotics are to support diversity in agriculture in the long term as reliable control systems. Our research focus is on the identification and development of assistance systems as well as on automated and field robotics applications. Simulation and IoT technologies are used to develop and improve digital twins, highly automated field sensor systems, field machines and field robotics.
To the team of the programme area
Research projects
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The quantiFARM project aims to develop cost-effective and mobile sensor systems to reduce environmentally harmful emissions in agriculture. By recording plant vitality and soil parameters, fertilisers and pesticides are …
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European soils face significant health challenges, with 60-70% deemed unhealthy due to pollution, urbanization, intensive agriculture, and climate change. This degradation has severe economic, societal, and environmental…
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The diversification of crop production systems in combination with a reduction in the use of agrochemicals and an increase in landscape complexity has the potential to promote biodiversity at the field and landscape scal…
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The digiMan joint project aims to develop and test digital humus and nutrient management systems on modern farms. These farms operate in four different soil and climate zones in the states of Bavaria and Brandenburg. Par…
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Publications of the programme area
- Horf, M.; Gebbers, R.; Olfs, H.; Vogel, S. (2024): Effects of Sample Pre-Treatments on the Analysis of Liquid Organic Manures by Visible and Near-Infrared Spectrometry. Heliyon. : p. 27136. Online: https://doi.org/10.1016/j.heliyon.2024.e27136 1.0
- Horf, M.; Gebbers, R.; Olfs, H.; Vogel, S. (2024): Determining Nutrients, Dry Matter, and pH of Liquid Organic Manures Using Visual and Near-Infrared Spectrometry. Science of the Total Environment. (Januar): p. 168045. Online: https://doi.org/10.1016/j.scitotenv.2023.168045 1.0
- Drastig, K.; Zimmermann, B.; Ammon, C.; Jacobs, H. (2024): Water productivity and irrigation water demand of potatoes in Brandenburg (Germany) between 1902 and 2020. Potato Research. (2): p. 0. Online: https://doi.org/10.1007/s11540-024-09734-z 1.0
- Matavel, C.; Meyer-Aurich, A.; Piepho, H. (2024): Model-averaging as an accurate approach for ex-post economic optimum nitrogen rate estimation. Precision Agriculture. (June): p. 1324-1339. Online: https://doi.org/10.1007/s11119-024-10113-4 1.0
- Su, P.; Kang, H.; Peng, Q.; Wicaksono, W.; Berg, G.; Liu, Z.; Ma, J.; Zhang, D.; Cernava, T.; Liu, Y. (2024): Microbiome homeostasis on rice leaves is regulated by a precursor molecule of lignin biosynthesis. nature communications. : p. 1-23. Online: https://doi.org/10.1038/s41467-023-44335-3 1.0
- Navas, E.; Shamshiri, R.; Dworak, V.; Weltzien, C.; Fernandez, R. (2024): Soft Gripper for Small Fruits Harvesting and Pick and Place Operations. Frontiers in Robotics and AI. : p. 1330496. Online: https://doi.org/10.3389/frobt.2023.1330496 1.0
- Darvishi, A.; Yousefi, M.; Schirrmann, M.; Ewert, F. (2024): Exploring biodiversity patterns at the landscape scale by linking landscape energy and land use/land cover heterogeneity. Science of the Total Environment. : p. 170163. Online: https://doi.org/10.1016/j.scitotenv.2024.170163 1.0
- Wicaksono, W.; Mora, M.; Bickel, S.; Berg, C.; Kühn, I.; Cernava, T.; Berg, G. (2024): Rhizosphere assembly alters along a chronosequence in the Hallstätter glacier forefield (Dachstein, Austria). FEMS Microbiology Ecology. : p. 0. Online: https://doi.org/10.1093/femsec/fiae005 1.0
- Bareeva, D.; Höhne, M.; Warnecke, A.; Pirch, L.; Müller, K.; Rieck, K.; Bykov, K. (2024): Manipulating Feature Visualizations with Gradient Slingshots. arXiv. : p. 1-19. Online: https://doi.org/10.48550/arXiv.2401.06122 1.0
- Schmidinger, J.; Schröter, I.; Bönecke, E.; Gebbers, R.; Rühlmann, J.; Kramer, E.; Mulder, V.; Heuvelink, G.; Vogel, S. (2024): Effect of training sample size, sampling design and prediction model on soil mapping with proximal sensing data for precision liming. Precision Agriculture. : p. 1-27. Online: https://doi.org/10.1007/s11119-024-10122-3 1.0
More publications of the programme area