Predictive Analytics Group

The Predictive Analytics Group focuses on the investigation and application of machine learning methods for real-world challenges in agriculture, construction, and industrial systems. Our interdisciplinary research explores data-driven prediction and evaluation techniques to support sustainability, efficiency, and decision-making. With expertise in both methodological development and applied use cases, we contribute to advancing predictive analytics in complex environments. The group’s work is documented in peer-reviewed publications, including experimental studies and systematic reviews.

Current members: Jörg Leukel, Luca Scheurer, Adrian Stengle
Former members: Martin Riekert, Tobias Zimpel

• Leukel, J., Scheurer, L., & Zimpel, T. (2025). Overinterpretation of evaluation results in machine learning studies for maize yield prediction: A systematic review. Computers and Electronics in Agriculture, 230, Article 109892, https://doi.org/10.1016/j.compag.2024.109892

• Müller, M., Gohl, S., Groll, K., & Leukel, J. (2025). Wie GreenAI-Bauprozesssteuerung und automatisierte CO2-Bilanzierung die CO2-Emissionen senken. In Schäfer, F. (Ed.), 4. Kolloquium Straßenbau in der Praxis: Fachtagung zum Planen, Bauen, Erhalten, Betreiben unter den Aspekten von Nachhaltigkeit und Digitalisierung. Tagungshandbuch 2025 (pp. 381-387). expert.
• Scheurer, L., Leukel, J., Zimpel, T., Werner, J., Perdana-Decker, S., & Dickhoefer, U. (2024). Predicting herbage biomass on small-scale farms by combining sward height with different aggregations of weather data. Agronomy Journal, 116(6), 3205-3221. https://doi.org/10.1002/agj2.21705
• Leukel, J., Scheurer, L., & Sugumaran, V. (2024). Machine learning models for predicting physical properties in asphalt road construction: A systematic review. Construction and Building Materials, 440, Article 137397.  https://doi.org/10.1016/j.conbuildmat.2024.137397
• Riekert, M. (2024). Automatische Prozessüberwachung in der Nutztierhaltung: Gestaltung eines Verfahrens zur Extraktion von Prozessindikatoren aus Bilddaten mittels Deep Learning. Dissertation, Universität Hohenheim. https://doi.org/10.60848/10815
• Stumpe, C., Leukel, J., & Zimpel, T. (2024). Prediction of pasture yield using machine learning-based optical sensing: A systematic review. Precision Agriculture, 25(1), 430-459. https://doi.org/10.1007/s11119-023-10079-9
• Zimpel, T., Perdana-Decker, S., Leukel, J., Scheurer, L., Dickhoefer, U., & Werner, J. (2023). P42 Estimating pasture yield using machine learning and weather data: Effect of small and large prediction horizons. Animal-science proceedings, 14(4), 628-629. https://doi.org/10.1016/j.anscip.2023.04.137
• Leukel, J., González, J., & Riekert, M. (2023). Machine learning-based failure prediction in industrial maintenance: Improving performance by sliding window selection. International Journal of Quality & Reliability Management, 40(6), 1449-1462. https://doi.org/10.1108/IJQRM-12-2021-0439
• Leukel, J., Zimpel, T., & Stumpe, C. (2023). Machine learning technology for early prediction of grain yield at the field scale: A systematic review. Computers and Electronics in Agriculture, 207, Article 107721. https://doi.org/10.1016/j.compag.2023.107721
• Zimpel, T. (2022). Modeling pig rearing as a digital shadow. In J. Michael, J. Pfeiffer, & A. Wortmann (Eds.), Modellierung 2022 Satellite Events. GI. https://doi.org/10.18420/modellierung2022ws-014
• Zimpel, T., Wild, A., Schrade, H., & Kirn, S. (2022). Association rule mining to study process-related cause-effect-relationships in pig farming. In Proceedings of the Workshop on Process Management in the AI Era 2022 (PMAI 2022) co-located with 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAIS 2022), Vienna, Austria, July 23, 2022. CEUR Workshop Proceedings 3310. https://ceur-ws.org/Vol-3310
• Leukel, J., González, J., & Riekert, M. (2021). Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review. Journal of Manufacturing Systems, 61, 87-96. https://doi.org/10.1016/j.jmsy.2021.08
• Riekert, M., Opderbeck, S., Wild, A., & Gallmann, E. (2021). Model selection for 24/7 pig position and posture detection by 2D camera imaging and deep learning. Computers and Electronics in Agriculture, 187, Article 106213. https://doi.org/10.1016/j.compag.2021.106213
• Riekert, M., Riekert, M. & Klein, A. (2021). Simple baseline machine learning text classifiers for small datasets. SN Computer Science, 2, Article 178. https://doi.org/10.1007/s42979-021-00480-4
• Zimpel, T., Riekert, M., Klein, A., & Hoffmann, C. (2021). Machine learning for predicting animal welfare risks in pig farming. Agricultural Engineering, 76(1), 24-35. https://doi.org/10.15150/lt.2021.3261
• Riekert, M., Klein, A., Adrion, F., Hoffmann, C., & Gallmann, E. (2020). Automatically detecting pig position and posture by 2D camera imaging and deep learning. Computers and Electronics in Agriculture, 174, Article 105391. https://doi.org/10.1016/j.compag.2020.105391
• Riekert, M., Zimpel, T., Hoffmann, C., Wild, A., Gallmann, E., & Klein, A. (2020). Towards animal welfare monitoring in pig farming using sensors and machine learning. In Referate der 40. Jahrestagung der Gesellschaft für Informatik in der Land-, Forst- und Ernährungswirtschaft. Fokus: Digitalisierung für Mensch, Umwelt und Tier (pp. 271-276). Freising, Germany. LNI P-299. GI.
• Zimpel, T., Riekert, M., Hoffmann, C., & Wild, A. (2020). Maschinelle Lernverfahren zur frühzeitigen Prognose der Handelsklasse. In Referate der 40. Jahrestagung der Gesellschaft für Informatik in der Land-, Forst- und Ernährungswirtschaft. Fokus: Digitalisierung für Mensch, Umwelt und Tier (pp. 361-366). Freising, Germany. LNI P-299. GI.
• Zimpel, T., Riekert, M., & Wild, A. (2020). Designing a smart farming platform for sustainable decision making. Proceedings of the 15th International Conference on Wirtschaftsinformatik (WI 2020). Potsdam, Germany. https://doi.org/10.30844/wi_2020_x3-zimpel