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Forschungsprogramm des GraKo

Das Forschungsprogramm des Graduiertenkollegs des Joint Lab wird durch die Forschungsschwerpunkte
(1) Künstliche Intelligenz und Data Science - Methodenentwicklung und Datenanalyse,
(2) Autonome Systeme - Sensornetzwerke, Robotersteuerung und Datenerfassung, sowie
(3) Hybride Prozess- und Systemmodellierung - Anwendungsbasierte Entwicklung von 
     Prozesssteuerungen und Digitalen Zwillingen gebildet. Diese Schwerpunkte umfassen insbesondere 
     die folgenden Forschungsthemen:

• Explainable AI Methods for Multimodal Data in the Bioeconomy
• Trustworthy Intelligent Soil Mapping
• Informed Machine Learning on Sparse Data and Information in the context of barn climate and emissions
• Data integration and processing platform using data science and machine learning techniques for
  real time prediction of dairy cattle health and welfare changes
• Simulation of Environment Sensors – towards digital twins of autonomous agricultural machines
• Auto-ML with synthetic training data and hardware in the Loop
• Deployment Strategies for in-situ Sensors in Agriculture
• Network as a Sensor in Agriculture
• Reconfigurable ROS Nodes for Modular Agricultural Robots
• Interpretable machine learning methods for identifying plant stress in crops by using and optimizing UAV data
• Estimation and prediction of biophysical crop parameters and water-deficit stresses, using multi-sensor satellite data and AI
• Integrated AI analysis of geometric and spectral UAV data using machine learning to derive high-resolution bio-physical and bio-chemical plant properties in agroforestry systems
• Monitoring biodiversity of agroforestry systems, using multisensor Earth-Observation data and deep learning
• Intelligent Processes for High-Quality Food Production
• Digital twins for sensor data-based drying of biomass under changing outdoor weather conditions
• Development of process models for bioconversion
• Modelling and efficient processing of complex sensor data for knowledge discovery and real-time change detection of fruit respiration rate during long-term cold storage
• Developing a digital twin for strategic decisions at the farm scale
• Development of predictive models to control the anaerobic digestion process
• Linking digital twins to individual-based models to predict heat stress effects on dairy cattle