Polina Kurtser

I am a post-doctoral researcher at Mobile Robotics and Olfaction Lab (MR&O) at Örebro University. Before to joining the MR&O lab in April 2019, I completed my PhD at the Department of Industrial Engineering and Management, Ben-Gurion University of the Negev (BGU) in Beer-Sheva, Israel. I have a Bachelors degree in Biomedical Engineering and a Masters degree in Industrial Engineering and Management from Ben-Gurion University of the Negev. I’ve been a short term visiting scholar at Forschungszentrum Jülich, Germany (2013) and Umeå University, Sweden (2017).

My research interests include computer vision, statistical analysis and machine learning techniques for agricultural robotic and medical imaging applications. I currently mainly focus on dynamic sensing, and information content estimation.

In my PhD I’ve been involved the EU Horizon 2020 project SWEEPER. The resulted product can be viewed here.

My full list of publications is available on my google scholar page. Some of them are outlined bellow.

Contact

Dr. Polina Kurtser

AASS Research Centre
School of Science and Technology
Örebro University
70182 Örebro, Sweden
Room T1213
Phone +46 (0)19 30 14 10
polina.kurtser@oru.se

Publications

Journal Articles

[1]	H. Gupta, A. Lilienthal, H. Andreasson and P. Kurtser. NDT-6D for color registration in agri-robotic applications. Journal of Field Robotics, 40(6):1603-1619, 2023 [ BibTeX \| DiVA ]
[2]	P. Kurtser and S. Lowry. RGB-D datasets for robotic perception in site-specific agricultural operations : A survey. Computers and Electronics in Agriculture, 212, 2023 [ BibTeX \| DiVA ]
[3]	H. Gupta, H. Andreasson, A. J. Lilienthal and P. Kurtser. Robust Scan Registration for Navigation in Forest Environment Using Low-Resolution LiDAR Sensors. Sensors, 23(10), 2023 [ BibTeX \| DiVA ]
[4]	P. Seeburger, A. P. F. Herdenstam, P. Kurtser, A. Arunachalam, V. Castro Alves, T. Hyötyläinen and H. Andreasson. Controlled mechanical stimuli reveal novel associations between basil metabolism and sensory quality. Food Chemistry, 404(Pt A), 2022 [ BibTeX \| DiVA ]
[5]	A. P. F. Herdenstam, P. Kurtser, J. Swahn and A. Arunachalam. Nature versus machine : A pilot study using a semi-trained culinary panel to perform sensory evaluation of robot-cultivated basil affected by mechanically induced stress. International Journal of Gastronomy and Food Science, 29, 2022 [ BibTeX \| DiVA ]
[6]	P. Kurtser, V. Castro Alves, A. Arunachalam, V. Sjöberg, U. Hanell, T. Hyötyläinen and H. Andreasson. Development of novel robotic platforms for mechanical stress induction, and their effects on plant morphology, elements, and metabolism. Scientific Reports, 11(1), 2021 [ BibTeX \| DiVA ]
[7]	L. v. Herck, P. Kurtser, L. Wittemans and Y. Edan. Crop design for improved robotic harvesting : A case study of sweet pepper harvesting. Biosystems Engineering, 192:294-308, 2020 [ BibTeX \| DiVA ]
[8]	B. Arad, J. Balendonck, R. Barth, O. Ben-Shahar, Y. Edan, T. Hellström, J. Hemming, P. Kurtser, O. Ringdahl, T. Tielen and B. van Tuijl. Development of a sweet pepper harvesting robot. Journal of Field Robotics, 37(6):1027-1039, 2020 [ BibTeX \| DiVA \| PDF ]
[9]	P. Kurtser, O. Ringdahl, N. Rotstein, R. Berenstein and Y. Edan. In-field grape cluster size assessment for vine yield estimation using a mobile robot and a consumer level RGB-D camera. IEEE Robotics and Automation Letters, 5(2):2031-2038, 2020 [ BibTeX \| DiVA ]
[10]	P. Kurtser and Y. Edan. Planning the sequence of tasks for harvesting robots. Robotics and Autonomous Systems, 131, 2020 [ BibTeX \| DiVA ]
[11]	E. Zemmour, P. Kurtser and Y. Edan. Automatic Parameter Tuning for Adaptive Thresholding in Fruit Detection. Sensors, 19(9), 2019 [ BibTeX \| DiVA ]
[12]	B. Arad, P. Kurtser, E. Barnea, B. Harel, Y. Edan and O. Ben-Shahar. Controlled Lighting and Illumination-Independent Target Detection for Real-Time Cost-Efficient Applications. The Case Study of Sweet Pepper Robotic Harvesting. Sensors, 19(6), 2019 [ BibTeX \| DiVA ]
[13]	O. Ringdahl, P. Kurtser and Y. Edan. Evaluation of approach strategies for harvesting robots : Case study of sweet pepper harvesting. Journal of Intelligent and Robotic Systems, 95(1):149-164, 2019 [ BibTeX \| DiVA \| PDF ]
[14]	M. Levi-Bliech, P. Kurtser, N. Pliskin and L. Fink. Mobile apps and employee behavior : An empirical investigation of the implementation of a fleet-management app. International Journal of Information Management, 49:355-365, 2019 [ BibTeX \| DiVA ]
[15]	P. Kurtser and Y. Edan. Statistical models for fruit detectability: spatial and temporal analyses of sweet peppers. Biosystems Engineering, 171:272-289, 2018 [ BibTeX \| DiVA ]
[16]	P. Kurtser and Y. Edan. The use of dynamic sensing strategies to improve detection for a pepper harvesting robot. IEEE International Conference on Intelligent Robots and Systems. Proceedings, pages 8286-8293, 2018 [ BibTeX \| DiVA ]

Refereed Conference and Workshop Articles

[1]	A. P. F. Herdenstam, P. Kurtser, J. Swahn, A. Arunachalam and K. M. Edberg. Nature versus machine : Sensory evaluation of robot-cultivated basil affected by mechanically induced stress. 2022 [ BibTeX \| DiVA \| PDF ]
[2]	P. Kurtser, O. Ringdahl, N. Rotstein and H. Andreasson. PointNet and geometric reasoning for detection of grape vines from single frame RGB-D data in outdoor conditions. In Proceedings of the Northern Lights Deep Learning Workshop, 1:1-6, 2020 [ BibTeX \| DiVA \| PDF ]
[3]	P. Kurtser, U. Hanell and H. Andreasson. Robotic Platform for Precise Mechanical Stress Induction in Greenhouses Cultivation. In 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), pages 1558-1565, 2020 [ BibTeX \| DiVA ]
[4]	O. Ringdahl, P. Kurtser and Y. Edan. Performance of RGB-D camera for different object types in greenhouse conditions. In 2019 European Conference on Mobile Robots (ECMR), pages 1-6, 2019 [ BibTeX \| DiVA ]
[5]	M. Levi-Bliech, P. Kurtser, N. Pliskin and L. Fink. The effects of a fleet-management app on driver behavior. 2018 [ BibTeX \| DiVA ]
[6]	E. Zemmour, P. Kurtser and Y. Edan. Dynamic thresholding algorithm for robotic apple detection. In 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pages 240-246, 2017 [ BibTeX \| DiVA ]
[7]	O. Ringdahl, P. Kurtser and Y. Edan. Strategies for selecting best approach direction for a sweet-pepper harvesting robot. In Towards Autonomous Robotic Systems (Taros 2017), pages 516-525, 2017 [ BibTeX \| DiVA \| PDF ]
[8]	O. Ringdahl, P. Kurtser, R. Barth and Y. Edan. Operational flow of an autonomous sweetpepper harvesting robot. 2016 [ BibTeX \| DiVA \| PDF ]
[9]	P. Kurtser, B. Arad, O. Ben-Shahar, M. van Bree, J. Moonen, B. van Tujil and Y. Edan. Robotic data acquisition of sweet pepper images for research and development. 2016 [ BibTeX \| DiVA \| PDF ]
[10]	B. Harel, P. Kurtser, L. van Herck, Y. Parmet and Y. Edan. Sweet pepper maturity evaluation via multiple viewpoints color analyses. , pages 1-7, 2016 [ BibTeX \| DiVA \| PDF ]
[11]	P. Kurtser, O. Levi and V. Gontar. Detection and classification of ECG chaotic components using ANN trained by specially simulated data. In Engineering Applications of Neural Networks, 311(311):193-202, 2012 [ BibTeX \| DiVA ]

Find the complete BibTeX record here.