Researching for a sustainable primary production
The research program is dedicated to site-specific sustainable intensification in the area of primary agricultural production all the way to harvesting.
Our research ranges from a comprehensive online data collection to modelling and process control. Technological and process engineering tasks include sensor-based technologies for precision farming and precision horticulture, the modelling of emissions and of the microclimate in naturally ventilated barn systems as well as the interaction between housing environment and animal welfare. The system assessment is analysing interactions and effects in relation to environment and economy. Here, we concentrate on nitrogen, greenhouse gases and water.
One focus of our research is to develop and apply sensors for assessing the condition of soils, plants and animals. Information on system parameters such as nutrient supply, plant growth, disease pressure, climatic conditions, water demand, respiratory rate, heat stress, fruit ripeness or others can be recorded. Information collected in-situ integrates into the development of complex physiological and physical models. Online analysis is an essential element of individual and flexible process control.
Precision crop production
Crop production is of central importance for the bio-economy: plant biomass not only provides food and animal feed, but is also the basis for bio-based materials and energy.
The global sustainability goals outline the current challenges for precision crop production: to increase productivity while using natural resources in a sustainable manner, to preserve biodiversity, ecosystems, soil fertility and natural habitats, to reduce the impact of invasive species and to ensure that chemicals are managed in an environmentally sound manner.
We are thus working on solutions for sensor-supported local resource management in precision agriculture and precision horticulture. The aim of our research is to increase the efficiency of plant production, in particular by means of adaptive process control and the development of technical solutions for individualized plant production, thus reducing consumption of natural resources, the use of chemicals and of emissions.
Innovative sensor technologies are opening up new possibilities in the field of data acquisition (information and communication technology, telemetry and robotics), processing (Big Data) and analysis of data (genomics, phenomics and bioinformatics). They are fundamental tools in the digital transformation process that agriculture is facing. The information obtained is used to develop complex physiological and physical models that enable precise control of production processes in the sense of a sustainable intensification.
Livestock Management
Animal husbandry concepts should integrate the three pillars of sustainability - environment, society and economy. Solutions have to be found in the area of conflicting interests. While the public's desire for improved animal welfare and more environmental protection is growing, the economic imperatives for the survival of farmers must also be taken into account to ensure that innovative processes can find their way into practice.
We therefore focus our application-oriented basic research in animal husbandry on the improvement of animal welfare, housing environment, animal and environmental protection and on maintaining economic competitiveness. Our goals are: objective animal welfare standards, concepts for solving environmental conflicts, transparent animal husbandry, consumer acceptance and added value from a regional production.
To the team of the research program 'Precision farming in crop and livestock management'
![[Translate to English:]](/fileadmin/generell/presse/Hopfen_AdobeStock_283385995.jpeg "[Translate to English:]")
Research projects
-
FAIRagro is bringing together the agrosystems research community and developing a customised, digital infrastructure.
-
Project within the framework of the mobility funding programme of the Sino-German Center for Research Promotion with researchers from Sun Yat-sen University and the ATB or the University of Potsdam on the topic of Reliab…
-
At the Leibniz Innovation Farm for Sustainable Bioeconomy, innovative concepts and technologies for a sustainable, circular bioeconomy will be developed and tested in future by combining agriculture with crop cultivation…
-
The aim of the Explaining 4.0 project is to develop methods that make a significant contribution to a holistic -global- understanding of AI models. Efficiency (through a priori knowledge), comprehensibility (through sema…
-
Report from the conference: Between June 30 and July 5, the Sino-German Agricultural Centre (DCZ) Science & Technology (S&T) platform in cooperation with the Chinese Academy of Agricultural Sciences (CAAS) and the Leibni…
More projects within the research program 'Precision farming in crop and livestock production'
Publications of the program
- Savolainen, T.; Giovannini, G.; Kovalev, Y.; Perucho, M.; Anderson, J.; Bruni, G.; Edwards, P.; Fuentes, A.; Giroletti, M.; Gómez, J.; Hada, K.; Lee, S.; Lisakov, M.; Lobanov, A.; López-Miralles, J.; Orienti, M.; Petrov, L.; Plavin, A.; Sohn, B.; Sokolovsky, K.; Voitsik, P.; Zensus, J. (2023): RadioAstron discovery of a mini-cocoon around the restarted parsec-scale jet in 3C 84*. Astronomy & Astrophysics (A&A). (August): p. 1-29. Online: https://doi.org/10.1051/0004-6361/202142594 1.0
- Hanfeld, P.; Höhne, M.; Bussmann, M.; Hönig, W. (2023): Flying Adversarial Patches: Manipulating the Behavior of Deep Learning-based Autonomous Multirotors. arXiv. : p. 1-6. Online: https://doi.org/10.48550/arXiv.2305.12859 1.0
- Hanfeld, P.; Wahba, K.; Höhne, M.; Bussmann, M.; Hönig, W. (2023): Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches. arXiv. : p. 1-7. Online: https://doi.org/10.48550/arXiv.2308.00344 1.0
- Hedström, A.; Weber, L.; Lapuschkin, S.; Höhne, M. (2023): Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test. arXiv. : p. 1-19. Online: https://arxiv.org/abs/2401.06465 1.0
- Gautam, S.; Höhne, M.; Hansen, S.; Jenssen, R.; Kampffmeyer, M. (2023): This looks More Like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation. Pattern Recognition. (April): p. 109172. Online: https://doi.org/10.1016/j.patcog.2022.109172 1.0
- Janke, D.; Bornwin, M.; Coorevits, K.; Hempel, S.; van Overbeke, P.; Demeyer, P.; Rawat, A.; Declerck, A.; Amon, T.; Amon, B. (2023): A low-cost wireless sensor network for barn climate and emission monitoring. Atmosphere. (14): p. 1643. Online: https://www.mdpi.com/2073-4433/14/11/1643 1.0
- Bommer, P.; Kretschmer, M.; Hedström, A.; Bareeva, D.; Höhne, M. (2023): Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science. arXiv. : p. 1-30. Online: https://doi.org/10.48550/arXiv.2303.00652 1.0
- Kim, J.; Savolainen, T.; Voitsik, P.; Kravchenko, E.; Lisakov, M.; Kovalev, Y.; Müller, H.; Lobanov, A.; Sokolovsky, K.; Bruni, G.; Edwards, P.; Reynolds, C.; Bach, U.; Gurvits, L.; Krichbaum, T.; Hada, K.; Giroletti, M.; Orienti, M.; Anderson, J.; Lee, S.; Sohn, B.; Zensus, J. (2023): RadioAstron Space VLBI Imaging of the jet in M87: I. Detection of high brightness temperature at 22 GHz. arXiv. : p. 1-27. Online: https://doi.org/10.48550/arXiv.2304.09816 1.0
- Kim, J.; Savolainen, T.; Voitsik, P.; Kravchenko, E.; Lisakov, M.; Kovalev, Y.; Müller, H.; Lobanov, A.; Sokolovsky, K.; Bruni, G.; Edwards, P.; Reynolds, C.; Bach, U.; Gurvits, L.; Krichbaum, T.; Hada, K.; Giroletti, M.; Orienti, M.; Anderson, J.; Lee, S.; Sohn, B.; Zensus, J. (2023): RadioAstron Space VLBI Imaging of the Jet in M87. I. Detection of High Brightness Temperature at 22 GHz. The Astrophysical Journal. (1): p. 1-17. Online: https://doi.org/10.3847/1538-4357/accf17 1.0
- Beltran, I.; van der Weerden, T.; Alfaro, M.; Amon, B.; de Klein, C.; Grace, P.; Hafner, S.; Hassouna, M.; Hutchings, N.; Krol, D.; Leytem, A.; Noble, A.; Salazar, F.; Thorman, R.; Velthof, G. (2021): Dataman: A global database of nitrous oxide and ammonia emission factors for excreta deposited by livestock and land-applied manure. Journal of Environmental Quality. (2): p. 513-527. Online: https://doi.org/10.1002/jeq2.20186 1.0
More publications of the research program