People

Marko Panić

Marko Panić

Position:Research associate

Academic Rank: Research associate in the field of technical/technological sciences- information technologies

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Dr. Marko Panić is a research associate in the field of technical sciences and information technologies at the BioSense Institute. His doctoral dissertation is related to the development of algorithms for accelerated image reconstruction from incomplete measurements using a sparse representation of signals and statistical Markov random models. His research direction includes statistical modeling of multidimensional data obtained from different sensors. He is currently working on the analysis and processing of images obtained from hyperspectral and multispectral cameras using advanced machine learning methods with application in assessing the quality of fruits and vegetables. As a member of the BioSense team, he participated in Syngenta’s “Crop Challenge” competition, which resulted in winning first place. He is actively involved in many projects from the Horizon 2020 call, including Antares, Cybele, Dragon and Flexirobots, as well as in projects with companies such as Krivaja, MK Agri and Delta Agrar.

Center:

proba 2

CIT

Themes:

1. FastMast – instrument for early detection of mastitis

2. Environmental monitoring applications

3. Government

4. Hyperspectral, Multispectral and Thermal Imaging

5. Drone Imaging

6. Satellite imaging

7. Geospatial data analytics

8. Deep Learning

Projects:

1. NANOFACTS – Networking Activities for Nanotechnology-Facilitated Cancer Theranostics
2. BREATHE – Real-time detection and quantification of bioaerosols relevant for human and plant health
3. ANTARES- Centre of Excellence for Advanced Technologies in Sustainable Agriculture and Food Security
4. DRAGON -Data Driven Precision Agriculture Services and Skill Acquisition
5. CYBELE-FOSTERING PRECISION AGRICULTURE AND LIVESTOCK FARMING THROUGH SECURE ACCESS TO LARGE-SCALE HPC-ENABLED VIRTUAL INDUSTRIAL EXPERIMENTATION ENVIRONMENT EMPOWERING SCALABLE BIG DATA ANALYTICS
6. agROBOfood- Business-Oriented Support to the European Robotics and Agri-food Sector, towards a network of Digital Innovation Hubs in Robotics
7. EUREKA WaQuMoS – Water Quality Monitoring System with Multi-Parameter IoT Sensor Nodes for Proactive Water Resource Management, 2019-2022, E!13044
8. FLEXIGROBOTS-Flexible robots for intelligent automation of precision agriculture operations
9. The use of satellite images during the growing season in order to visualize crop variation and develop maps for rational variable nitrogen supplementation
10. AITool4WYP – Artificial intelligence-driven tool for early wheat yield prediction 
11. Smart-AKIS- European Agricultural Knowledge and Innovation Systems (AKIS) towards innovation-driven research in Smart Farming Technology
12. KATANA – Emerging industries as key enablers for the adoption of advanced technologies in the agrifood sector
13. Code: Re-farm – Consumer-driven demands to reframe farming systems
14. INNO-4-AGRIFOOD- Capitalising the full potential of on-line collaboration for SMEs innovation support in the Agri-Food ecosystem
15. Optimization of sampling location and number of soil samples based on processing of satellite images of plots and zoning in order to reduce costs
16. Detection of usurped state-owned agricultural land and detection of burning of crop residues on the territory of APV
17. Analysis of collected data using unmanned aerial vehicles

Publications:

  1. Panić, M., Aelterman, J., Crnojević, V. and Pižurica, A., 2017. Sparse recovery in magnetic resonance imaging with a Markov random field prior. IEEE transactions on medical imaging, 36(10), pp.2104-2115.
  2. Panić, M., Jakovetić, D., Vukobratović, D., Crnojević, V. and Pižurica, A., 2020. MRI Reconstruction Using Markov Random Field and Total Variation as Composite Prior. Sensors, 20(11), p.3185.
  3. Panić, M., Aelterman, J., Crnojević, V. and Pižurica, A., 2016, August. Compressed sensing in MRI with a Markov random field prior for spatial clustering of subband coefficients. In 2016 24th European Signal Processing Conference (EUSIPCO) (pp. 562-566). IEEE.
  4. Marko, O., Brdar, S., Panić, M., Šašić, I., Despotović, D., Knežević, M. and Crnojević, V., 2017. Portfolio optimization for seed selection in diverse weather scenarios. PloS one, 12(9), p.e0184198.
  5. Crnojević, V., Panić, M., Brkljač, B., Ćulibrk, D., Ačanski, J. and Vujić, A., 2014. Image processing method for automatic discrimination of hoverfly species. Mathematical Problems in Engineering, 2014.

Patent:

- A method for controlling water quality in the simultaneous presence of bacteria, sludge and algae by a heuristic approach

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

- System and method for intelligent soil sampling

- Universal system and method for automatic classification of pollen particle types in air in real time