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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Digitalisation, robotics, and smart algorithms have brought the data economy to the farm. However, farms face large societal pressures related to soil: cleaner water, healthier soils, improved carbon storage and increase…
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The Soil-X-Change Initiative aims to facilitate knowledge exchange and collaboration on sustainable soil and farm management. It will connect farmers, policymakers, projects, and initiatives to accelerate innovation and …
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The POUNDER project focuses on the adaptation of urban ponds to pollution to enhance the resilience of aquatic ecosystems. It aims to explore eco-evolutionary dynamics and ecosystem resilience in the face of environmenta…
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Europe faces escalating challenges including more frequent droughts, heatwaves, and emerging diseases, prompting a shift from current agrochemical and water-dependent agricultural practices. CropBiomes leads this transit…
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In Brandenburg, there are extensive lowland moor areas with a great diversity of habitats, which are, however, almost completely drained and subject to progressive degradation. There is preliminary work on rewetting in s…
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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
- Matavel, C.; Meyer-Aurich, A.; Piepho, H. (2024): Model-averaging as an accurate approach for economic optimum nitrogen rate estimation. Precision Agriculture. (2): p. 0. 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
- Mbogning, S.; Okiobe, S.; Theuerl, S.; Nwaga, D. (2024): Synergistic interplay between arbuscular mycorrhizal fungi and fern manure compost tea suppresses common tomato phytopathogens and pest attacks on-farm. Frontiers in Horticulture. : p. 1253. Online: https://doi.org/10.3389/fhort.2024.1253616 1.0
More publications of the programme area