Lukas Minogue

Lukas Minogue is a researcher at the Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Potsdam, affiliated with their Sensor Technology and Modeling department. He holds a Master's degree in Mathematics (University of Potsdam, 2019-2023) with focus on machine learning and statistical data analysis, and a prior Bachelor's degree in Mathematics (University of Potsdam, 2013-2019) concentrating on statistical modeling.

His research specializes in applying machine learning models to agricultural sensor data, particularly for mastitis detection in dairy cows through automated milking systems (AMS). Key projects include:

• MEDICow (German-Irish collaboration): Developing AI-driven mastitis risk assessment using multisensor data
• AMRAfrica: Strengthening antibiotic resistance monitoring through data management systems in DR Congo
• INNOVAFRIKA (DFG-funded): Digital innovations in crop-livestock systems

Recent publications demonstrate his work on:

• Time series analysis of AMS data with ML classifiers (logistic regression, random forest, neural networks)
Comparative performance of SMOTE resampling techniques
• Feature engineering for dairy health metrics (e.g., fat-protein ratio)

His technical expertise spans Python (scikit-learn, NumPy), data visualization (Matplotlib), and statistical modeling frameworks.