Topinformationen

Viacheslav Barkov (Slava)

PhD student

E-Mail

Coppenrath Innovation Centre
Hamburger Straße 24
LOK 15, Raum 01.13.03A
49084 Osnabrück
Tel.:  +49 541 969-6341

Modeling Sensor Data for Knowledge Discovery and Explainable Decision-Making

As the volume and complexity of agricultural sensor data grows, an increasing need emerges for advanced predictive modeling, knowledge discovery, and explainable decision-making in agricultural contexts. This research focuses on developing state-of-the-art machine learning and deep learning methods to effectively process, analyze, and interpret diverse agricultural proximal and remote sensor data. The goal of the project is to contribute to the fields of AI, machine learning, and agriculture by developing methods that not only enhance prediction accuracy but also provide clear, interpretable insights into the underlying patterns and relationships in agricultural data. One of the key areas that this research explores to improve is pedometrics and digital soil mapping, aiming to advance our understanding and prediction capabilities in soil science through modern machine learning approaches.

Project team: Viacheslav Barkov (Ph.D. Student), Prof. Dr. Martin Atzmüller (UOS), Dr. agr. Robin Gebbers (ATB)

Publications

2024

• V. Barkov, J. Schmidinger, R. Gebbers, and M. Atzmueller, “An efficient model-agnostic approach for uncertainty estimation in data-restricted pedometric applications,” 2024. https://doi.org/10.48550/arXiv.2409.11985
• J. Schmidinger, V. Barkov, H. Tavakoli, J. Correa, M. Ostermann, M. Atzmueller, R. Gebbers, and S. Vogel, “Which and how many soil sensors are ideal to predict key soil properties: A case study with seven sensors,” Geoderma, vol. 450, p. 117017, 2024. http://doi.org/10.1016/j.geoderma.2024.117017
• M. Lucas, V. Barkov, R. Pecenka, M. Atzmueller, and B. Waske, “Mapping trees outside forests using semantic segmentation,” in IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, pp. 4435–4438, 2024. http://doi.org/10.1109/IGARSS53475.2024.10641035