Präzisionslandwirtschaft in Pflanzenbau und Tierhaltung

Aufsätze in referierten Fachzeitschriften [30 Ergebnisse]

• Schmidinger, J.; Barkov, V.; Vogel, S.; Atzmüller, M.; Heuvelink, G. (2025): Kriging prior regression: A case for kriging-based spatial features with TabPFN in soil mapping. Computers and Electronics in Agriculture. (March 2026): p. 111352. Online: https://doi.org/10.1016/j.compag.2025.111352
• Kaiser, L.; Hänel, T.; Vogel, S.; Aschenbruck, N. (2025): A long-term evaluation of soil moisture sensing based on wireless communication. Computers and Electronics in Agriculture. : p. 111054. Online: https://doi.org/10.1016/j.compag.2025.111054
• Rosso, P.; Huang, S.; Inforsato, L.; Bönecke, E.; Gebbers, R.; Vogel, S.; Rühlmann, J.; Kersebaum, K. (2025): Combining mobile proximal soil sensors and a crop model to produce high spatial resolution yield prediction maps. Precision Agriculture. : p. 79. Online: https://doi.org/10.1007/s11119-025-10274-w
• Torney, L.; Weltzien, C.; Herold, M.; Vogel, S.; Voß, S. (2025): Improving agricultural management zoning involving Sentinel-2 timeseries, crop’s phenology stages and proximal soil sensing data. Discover Agriculture. (113): p. 1-29. Online: https://doi.org/10.1007/s44279-025-00283-8
• Schmidinger, J.; Vogel, S.; Barkov, V.; Pham, A.; Gebbers, R.; Tavakoli, H.; Correa Reyes, J.; Tavares, T.; Filippi, P.; Jones, E.; Lukas, V.; Bönecke, E.; Rühlmann, J.; Schröter, I.; Kramer, E.; Pätzold, S.; Kodaira, M.; Wadoux, A.; Bragazza, L.; Metzger, K.; Huang, J.; Valente, D.; Safanelli, J.; Bottega, E.; Dalmolin, R.; Farkas, C.; Steiger, A.; Horst, T.; Ramirez-Lopez, L.; Scholten, T.; Stumpf, F.; Rosso, P.; Costa, M.; Zandonadi, R.; Wetterlind, J.; Atzmüller, M. (2025): LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping. Geoderma. (July): p. 117337. Online: https://doi.org/10.1016/j.geoderma.2025.117337
• Vogel, S.; Gebbers, M.; Schröter, I.; Schwanghart, W.; Bönecke, E.; Rühlmann, J.; Kramer, E.; Gebbers, R. (2025): Towards site-independent calibration of in situ soil pH sensor data: Relevance of spatial and temporal proximity, sample size and data spread for calibration model performance. Geoderma. (April 2025): p. 117261. Online: https://www.sciencedirect.com/science/article/pii/S0016706125000990
• Correa Reyes, J.; Tavakoli, H.; Vogel, S.; Gebbers, R. (2025): Overfitting due to data leakage in soil sensor calibration: Examples from lab-based and in-situ soil NIR spectroscopy. Computers and Electronics in Agriculture. (December): p. 110920. Online: https://doi.org/10.1016/j.compag.2025.110920
• Tavakoli, H.; Correa Reyes, J.; Vogel, S.; Oertel, M.; Zimne, M.; Heisig, M.; Harder, A.; Wruck, R.; Pätzold, S.; Leenen, M.; Gebbers, R. (2024): The RapidMapper: State-of-the-art in mobile proximal soil sensing based on a novel multi-sensor platform. Computers and Electronics in Agriculture. (November): p. 109443. Online: https://www.sciencedirect.com/science/article/pii/S0168169924008342
• Schmidinger, J.; Barkov, V.; Tavakoli, H.; Correa Reyes, J.; Ostermann, M.; Atzmüller, M.; Gebbers, R.; Vogel, S. (2024): Which and how many soil sensors are ideal to predict key soil properties: A case study with seven sensors. Geoderma. (October): p. 117017. Online: https://doi.org/10.1016/j.geoderma.2024.117017
• Vogel, S.; Emmerich, K.; Schröter, I.; Bönecke, E.; Schwanghart, W.; Rühlmann, J.; Kramer, E.; Gebbers, R. (2024): The effect of soil moisture content and soil texture on fast in situ pH measurements with two types of robust ion-selective electrodes. Soil. (1): p. 321-333. Online: https://doi.org/10.5194/soil-10-321-2024