Precision farming in crop and livestock productionPhoto: ATB
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.
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.
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'
IRRIWELL – A novel plant-based approach to estimate irrigation water needs and apply optimal deficit strategy - Specific project: Reference data for sensor calibration and implementation of sensor data in physio… ▶
The main goal of IRRIWELL is to test the implementation of a novel approach to estimate water requirements of fruit trees based on stomatal conductance with the aid of plant sensors and mechanistic physiological models. …
The adaptation of RES technologies and machinery and their demonstration at a large-scale on farm level, require supporting measures with respect to spatial planning, infrastructure, different business models and market …
SustInAfrica – Sustainable intensification of food production through resilient farming systems in West & North Africa ▶
Large areas of agricultural land in W. and N. Africa are heavily degraded, with water scarcity, low soil fertility and poor plant health, due to use of unsuitable agronomic systems and inappropriate management. In W. Afr…
The targeted use of crop protection agents as required according to the occurrence of pests requires sensor-supported monitoring of the crop fields. The project aims to develop sensor-based methods that enable automated …
SMART4ALL – Self-sustained customized cyberphysical system experiments for capacity building among European stakeholders (SMART4ALL) ▶
SMART4ALL builds capacity amongst European stakeholders via the development of selfsustained, cross-border experiments that transfer knowledge and technology between academia and industry. It targets CLEC CPS and the IoT…
More projects within the research program 'Precision farming in crop and livestock production'
- Cárdenas, A.; Ammon, C.; Schumacher, B.; Stinner, W.; Herrmann, C.; Schneider, M.; Weinrich, S.; Fischer, P.; Amon, T.; Amon, B. (2021): Methane emissions from storage of liquid dairy manure: influence of season, temperature and storage duration. Waste Management. : p. 393-402. Online: https://doi.org/10.1016/j.wasman.2020.12.026 1.0
- Doumbia, E.; Janke, D.; Yi, Q.; Amon, T.; Kriegel, M.; Hempel, S. (2021): CFD modelling of an animal occupied zone using an anisotropic porous medium model with velocity depended resistance parameters. Computers and Electronics in Agriculture. (February 2021): p. 105950. Online: https://doi.org/10.1016/j.compag.2020.105950 1.0
- Tavakoli, H.; Alirezazadeh, P.; Hedayatipour, A.; Banijamali Nasib, A.; Landwehr, N. (2021): Leaf image-based classification of some common bean cultivars using discriminative convolutional neural networks. Computers and Electronics in Agriculture. (February): p. 105935. Online: https://doi.org/10.1016/j.compag.2020.105935 1.0
- Wang, X.; Wu, J.; Yi, Q.; Zhang, G.; Amon, T.; Janke, D.; Li, X.; Chen, B.; He, Y.; Wang, K. (2021): Numerical evaluation on ventilation rates of a novel multi-floor pig building using computational fluid dynamics. Computers and Electronics in Agriculture. (March 2021): p. 106050. Online: https://www.sciencedirect.com/science/article/abs/pii/S0168169921000685?via%3Dihub 1.0
- Penzel, M.; Pflanz, M.; Gebbers, R.; Zude-Sasse, M. (2021): Tree adapted mechanical flower thinning prevents yield loss caused by over thinning of trees with low flower set in apple. European Journal of Horticultural Science. (1): p. 86-89. Online: https://doi.org/10.17660/eJHS.2021/86.1.10 1.0
- Jakob, M.; Geyer, M. (2021): Fruit removal forces of early stage pickling cucumbers for harvest automation. International Agrophysics. (1): p. 25-30. Online: https://doi.org/10.31545/intagr/131867 1.0
- Arsalan, M.; Khan, Z.; Sultan, M.; Ali, I.; Shakoor, A.; Mahmood, M.; Ahmad, M.; Shamshiri, R.; Imran, M.; Khalid, M. (2021): Experimental investigation of a wastewater treatment system utilizing maize cob as trickling filter media. Parlar Research and Technology. (1): p. 148-157. Online: https://www.prt-parlar.de/download_feb_2021/ 1.0
- Harfenmeister, K.; Itzerott, S.; Weltzien, C.; Spengler, D. (2021): Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data. Remote Sensing. (4): p. 575. Online: https://doi.org/10.3390/rs13040575 1.0
- Bobrowski, A.; Willink, D.; Janke, D.; Amon, T.; Hagenkamp-Korth, F.; Hasler, M.; Hartung, E. (2021): Reduction of ammonia emissions by applying a urease inhibitor in naturally ventilated dairy barns. Biosystems Engineering. (April 2021): p. 104-114. Online: https://doi.org/10.1016/j.biosystemseng.2021.01.011 1.0
- Schirrmann, M.; Landwehr, N.; Giebel, A.; Garz, A.; Dammer, K. (2021): Early Detection of Stripe Rust in Winter Wheat using Deep Residual Neural Networks. Frontiers in Plant Science. : p. 469689. Online: https://doi.org/10.3389/fpls.2021.469689 1.0