Dominik Bittner

SUSTAIN Cohort 1 Student

University

University of Aberdeen

SUSTAIN Project

Nature-based Solutions for Restoration of Degraded Soils in Sub-Saharan Africa

Supervisors

Prof Jo Smith (University of Aberdeen)

Prof Anil Fernando (University of Strathclyde)

Research Interests

Sustainable agriculture, soil health mapping, artificial intelligence

I am a first year PhD student in the SUSTAIN CDT program at the University of Aberdeen and I am very excited about my projects’ interdisciplinary approach, combining soil and computer science.
During my PhD, I will analyse different nature-based solutions, which aim to restore degraded soils, and evaluate their impact on Ethiopian agricultural lands’ soil quality using Artificial Intelligence. This will serve to advise Ethiopian farmers on which nature-based solutions are most appropriate for their respective areas, ultimately improving their lands’ resilience to severe droughts and rainfall.

Nature-based Solutions for Restoration of Degraded Soils in Sub-Saharan Africa

Prof Jo Smith, University of Aberdeen; Prof Anil Fernando, University of Strathclyde; Getahun Yakob, Southern Agricultural Research Institute (SARI), Ethiopia (Industry Supervisor), Moses Kimani, Lentera Ltd, Kenya (Adviser)

Natural systems are underpinned by soils, and nature and soils are mutually inter-dependent. This balance has been disrupted by our uses for land, especially in climatically vulnerable regions with sensitive soils, such as found in many places in Sub-Saharan Africa. A healthy soil provides resilience to climate shocks and extremes. Therefore, improving soil health is a key adaptation to climate change. However, many soils in Sub-Saharan Africa are degrading due to high levels of erosion, decreasing organic matter, salinisation, acidification and contamination, resulting in declining productivity, farm income and household well-being. This project will use machine-learning to investigate the use of nature-based solutions to improve soil health, so allowing increased resilience to climate change.

Dominik will collate data from Sub-Saharan Africa on impacts of nature-based solutions on soil health, water conservation and crop production. Machine-learning will be used to develop geographically-based software that will allow the impact of nature-based solutions on soil health and climate adaptation to be ranked for different soils, crops, environments and terrains in different locations. Dominik will hold stakeholder workshops with farmers in the Hawassa region of Ethiopia to understand requirements of farmers for the software (e.g. format of outputs, layout of software, available hardware, connectivity issues). The software will be packaged to meets requirement and tested with farmers. Finally, the downloaded application will be assessed more widely using automated online feedback procedures.

Publications:

Staufer, S., Hauser, F., Ezer, T., Grabinger, L., Nadimpalli, V. K., Röhrl, S., Bugert, F., Bittner, D., Mottok, J. (2024). LEARNING ELEMENTS IN LMS-A SURVEY AMONG STUDENTS. In EDULEARN2024 Proceedings (pp. 9089-9098). IATED.
Bittner, D., Nadimpalli, V. K., Grabinger, L., Ezer, T., Hauser, F., & Mottok, J. (2024, June). Uncovering Learning Styles through Eye Tracking and Artificial Intelligence. In Proceedings of the 2024 Symposium on Eye Tracking Research and Applications ETRA-2024.
Bittner, D., Ezer, T., Grabinger, L., Hauser, F., & Mottok, J. (2023). Eye Tracking based Learning Style Identification for Learning Management Systems (1.0.0) [Data set]. Zenodo.
Staufer, S., Hauser, F., Grabinger, L., Bittner, D., Nadimpalli, V. K., Bugert, F., Mottok, J. (2024). LEARNING ELEMENTS IN LMS-A SURVEY AMONG STUDENTS. In INTED2024 Proceedings (pp. 4224-4231). IATED.
Staufer, S., Bugert, F., Hauser, F., Grabinger, L., Ezer, T., Nadimpalli, V. K., Bittner, D., Röhrl, S., Mottok, J. (2024). TYCHE ALGORITHM: MARKOV MODELS FOR GENERATING LEARNING PATHS IN LEARNING MANAGEMENT SYSTEMS. In INTED2024 Proceedings (pp. 4224-4231). IATED.
Röhrl, S., Staufer, S., Nadimpalli, V. K., Bugert, F., Hauser, F., Grabinger, L., Bittner, D., Ezer, T., Mottok, J., (2024) PYTHIA - AI SUGGESTED INDIVIDUAL LEARNING PATHS FOR EVERY STUDENT, INTED2024 Proceedings, pp. 2871-2880.
Staufer, S., Hauser, F., Grabinger, L., Bittner, D., Nadimpalli, V. K., Bugert, F., Mottok, J. (2024). LEARNING ELEMENTS IN LMS-A SURVEY AMONG STUDENTS. In INTED2024 Proceedings (pp. 4224-4231). IATED.
Bittner, D., Ezer, T., Grabinger, L., Hauser, F., & Mottok, J. (2023, September). Unveiling the Secrets of Learning Styles: Decoding Eye Movements via Machine Learning. In Proceedings of the 16th annual International Conference of Education, Research and Innovation ICERI-2023.
Nadimpalli, V., Bugert, F., Bittner, D., Hauser, F., Grabinger, L., Staufer, S., & Mottok, J. (2023, September). Towards Personalized Learning Paths in Adaptive Learning Management Systems: Bayesian Modelling of Psychological Theories. In Proceedings of the 16th annual International Conference of Education,Research and Innovation ICERI-2023.
Staufer, S., Hauser, F., Grabinger, L., Bittner, D., Nadimpalli, V., & Mottok, J. (2023, September). Learning Elements in Online Learning Management Systems. In Proceedings of the 16th annual International Conference of Education,Research and Innovation ICERI-2023.
Bittner, D., Hendricks, R., Horn, L., & Mottok, J. (2023, July). In-depth Benchmarking of Transfer Learning Techniques for Improved Bottle Recognition. In Proceedings of 13th International Conference on Pattern Recognition Systems ICPRS- 2023. IEEE.
Bittner, D., Hauser, F., Nadimpalli, V. K., Grabinger, L., Staufer, S., & Mottok, J. (2023, June). Towards Eye Tracking based Learning Style Identification. In Proceedings of the 5th European Conference on Software Engineering Education ECSEE-2023 (pp. 138-147).
Nadimpalli, V. K., Hauser, F., Bittner, D., Grabinger, L., Staufer, S., & Mottok, J. (2023, June). Systematic Literature Review for the Use of AI Based Techniques in Adaptive Learning Management Systems. In Proceedings of the 5th European Conference on Software Engineering Education (pp. 83-92).
Bugert, F., Grabinger, L., Bittner, D., Hauser, F., Nadimpalli, V. K., Staufer, S., & Prof. Dr. Mottok, J. (2023, June). Towards Learning Style Prediction based on Personality. In Proceedings of the 5th European Conference on Software Engineering Education (pp. 48-55).
Bittner, D., Ferreira, J. F., Andrada, M. E., Bird, J. J., & Portugal, D. (2022, June). Generating synthetic multispectral images for semantic segmentation in forestry applications. In ICRA 2022 Workshop in Innovation in Forestry Robotics: Research and Industry Adoption.
Bittner, D., Andrada, M. E., Portugal, D., & Ferreira, J. F. (2021). SEMFIRE forest dataset for semantic segmentation and data augmentation (2.0) [Data set]. Zenodo.

Grants:

Eye Movement Modelling Examples (EMMEs) for public - EMME4Public (2024-04 - 2025-04); Bavarian State Ministry for Science and Art, Munich, DE; Grant Number: L.1-H2493.0/15; Grant volume: 60000€

Talks:

Guest lecture at University of Vienna on "Eye Tracking and Artificial Intelligence" in Dec. 2023
Presentation at ICRA-2022 Workshop in Innovation in Forestry Robotics: Research and Industry Adoption
Presentation at European Conference on Software Engineering Education ECSEE-2023
Presentation at International Conference on Pattern Recognition Systems ICPRS- 2023
Presentation at International Conference of Education, Research and Innovation ICERI-2023
Presentation at Symposium on Eye Tracking Research and Applications ETRA-2024

Posters:

Title: Towards use of AI-Powered Hybrid Soil Health Assessment to Design Nature-Based Solutions for Restoration of Degraded Soils in Sub-Saharan Africa. Authors: D. Bittner, J. Smith, G. Leontidis, A. Fernando. Conference: AI4SoilHealth, Budapest, Hungary, December 2024.

Welcome to the UKRI AI Centre for Doctoral Training in Sustainable Understandable agri-food Systems Transformed by Artificial INtelligence (SUSTAIN).