Oskar Marko

Position:Senior research associate

Academic Rank: Senior Research Associate in the field of technical/technological sciences-electronics, telecommunications, and information technologies

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Oskar Marko, Ph.D. is the Head of the Center for Information Technologies. His research focuses on applying advanced machine learning and evolutionary algorithms in agriculture. He spent the 3rd year of his undergraduate studies at City University London, where he did his final BEng project in signal processing. He led BioSense’s team that developed novel Big Data algorithms for yield prediction, seed distribution, and optimization of sowing strategies, which secured the 1st prize for BioSense at the Syngenta Crop Challenge and CGIAR Inspire Challenge. He is actively involved in many Horizon 2020 projects including Antares, Cybele, Dragon, and Flexigrobots, and industrial projects with partners such as Krivaja, MK Agriculture, Delta, and other farming and insurance companies.

Center:

CIT

Themes:

1. Machine Learning

2. Deep Learning

3. Big Data Analytics

4. Knowledge discovery

5. Sentinel Data Hub

6. Satellite imaging

7. Drone Imaging

8. Robotics

9. Hyperspectral, Multispectral and Thermal Imaging

10. Geospatial data analytics

11. AgroSense

12. New technologies are restoring the trust between producers and insurance companies

13. Government

Projects:

1. CYBELE-FOSTERING PRECISION AGRICULTURE AND LIVESTOCK FARMING THROUGH SECURE ACCESS TO LARGE-SCALE HPC-ENABLED VIRTUAL INDUSTRIAL EXPERIMENTATION ENVIRONMENT EMPOWERING SCALABLE BIG DATA ANALYTICS

2. DRAGON -Data Driven Precision Agriculture Services and Skill Acquisition

3. ANTARES- Centre of Excellence for Advanced Technologies in Sustainable Agriculture and Food Security

4. FLEXIGROBOTS-Flexible robots for intelligent automation of precision agriculture operations

5. agROBOfood- Business-Oriented Support to the European Robotics and Agri-food Sector, towards a network of Digital Innovation Hubs in Robotics

6. eLTER PLUS-European long-term ecosystem, critical zone and research infrastructure of socio-ecological systems PLUS

7. AgroSense APV

8. Optimization of sampling location and number of soil samples based on processing of satellite images of plots and zoning in order to reduce costs

9. AITool4WYP – Artificial intelligence-driven tool for early wheat yield prediction

10. The use of satellite images during the growing season in order to visualize crop variation and develop maps for rational variable nitrogen supplementation

11. Analysis of collected data using unmanned aerial vehicles

12. Detection of usurped state-owned agricultural land and detection of burning of crop residues on the territory of APV

13. Nostradamus – Data Cube and Copernicus data for Food Security and European Independence

14. FoodSafeR – A joined-up approach to the identification, assessment and management of emerging food safety hazards and associated risks

Publications:

Marko, O., Brdar, S., Panić, M., Šašić, I., Despotović, D., Knežević, M., & Crnojević, V. (2017). Portfolio optimization for seed selection in diverse weather scenarios. PloS one, 12(9), e0184198.
Marko, O., Brdar, S., Panic, M., Lugonja, P., & Crnojevic, V. (2016). Soybean varieties portfolio optimisation based on yield prediction. Computers and Electronics in Agriculture, 127, 467-474.
Marko, O., Pavlović, D., Crnojević, V., & Deb, K. (2019, July). Optimisation of crop configuration using NSGA-III with categorical genetic operators. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 223-224).
Šikoparija, B., Marko, O., Panić, M., Jakovetić, D., & Radišić, P. How to prepare a pollen calendar for forecasting daily pollen concentrations of Ambrosia, Betula and Poaceae?. Aerobiologia, 1-15.
O Marko, M Panić, P Lugonja, G Kitić, S Birgermajer, N Ljubičić, V Radonić, Software tool for smart irrigation based on machine learning, 2019

Patent:

- A method for early prediction of wheat yield based on artificial intelligence