mariiapukalchik



Personal Websites

http://edsel.skoltech.ru/

Mariia Pukalchik

Assistant Professor
Digital AgroLaboratory

Mariia Pukalchik is Assistant Professor in Digital Agriculture Lab (Skoltech)-based in Moscow, Russia. She has a Ph.D. in Ecology  (Environmental and soil sciences) from Lomonosov Moscow State University, Moscow. She joined Skoltech in 2018. Before joining Skoltech, Maria worked at the Czech University of Life Science (Faculty of Agrobiology, Food, and Natural Resources) as a postdoc fellow and industrial companies in Russia.

Now, Mariia is recognized as a leader in machine learning and artificial intelligence application in agricultural and environmental science. She has made a pioneering contribution by integrating advanced computer vision approaches in crop phenomics. As a Principal Investigator, she has provided strategic leadership to five projects supported by RFBR and RSF from 2013.

Areas of expertise: Agriculture, Environmental and climate changes, Machine Learning and AI applications, Soil, Water, Computer vision, Plants

Google Scholar: https://scholar.google.ru/citations?user=KoF5RkAAAAAJ&hl=en

Research Gate: https://www.researchgate.net/profile/M-Pukalchik

 

Now Dr. Mariia Pukalchik works in “Digital Agriculture” –  the new scientific field that uses data-intensive approaches to drive agricultural productivity with minimizing its environmental impact, and which have arisen from agro-technology and precision farming.  She focuses on the benefits and prospects of intelligent digital tools to advance connectivity in agriculture. 

1.Pukalchik, M. et al. The improvement of multi-contaminated sandy loam soil chemical and biological properties by the biochar, wood ash, and humic substances amendments. Environmental Pollution 229, 516–524 (2017).

2.Mirvakhabova, L., Pukalchik, M., Matveev, S., Tregubova, P. & Oseledets, I. Field heterogeneity detection based on the modified FastICA RGB-image processing. Journal of Physics: Conference Series 1117, 012009 (2018).

3.Panova, M. I., Pukalchik, M. A., Uchanov, P. V. & Terekhova, V. A. Influence of Lead Nitrate and Acetate Applied to Sod-Podzolic Soil on its Bioindicative Parameters. Biology Bulletin 45, 1293–1300 (2018).

4.Pukalchik, M. et al. Using humic products as amendments to restore Zn and Pb polluted soil: a case study using rapid screening phytotest endpoint. Journal of Soils and Sediments 18, 750–761 (2018).

5.Pukalchik, M., Mercl, F., Terekhova, V. & Tlustoš, P. Biochar, wood ash and humic substances mitigating trace elements stress in contaminated sandy loam soil: Evidence from an integrative approach. Chemosphere 203, 228–238 (2018).

6.Terekhova, V. A., Verkhovtseva, N. V., Pukalchik, M. A., Vodolazov, I. R. & Shitikov, V. K. Chemodiagnostic by Lipid Analysis of the Microbial Community Structure in Trace Metal Polluted Urban Soil. in Megacities 2050: Environmental Consequences of Urbanization (eds. I. Vasenev, V., Dovletyarova, E., Chen, Z. & Valentini, R.) 150–160 (Springer International Publishing, 2018). doi:10.1007/978-3-319-70557-6_16.

7.Nikitin, A., Fastovets, I., Shadrin, D., Pukalchik, M. & Oseledets, I. Bayesian optimization for seed germination. Plant Methods 15, (2019).

8.Pukalchik, M. A., Terekhova, V. A., Karpukhin, M. M. & Vavilova, V. M. Comparison of Eluate and Direct Soil Bioassay Methods of Soil Assessment in the Case of Contamination with Heavy Metals. Eurasian Soil Science 52, 464–470 (2019).

9.Pukalchik, M. A., Katrutsa, A. M., Shadrin, D., Terekhova, V. A. & Oseledets, I. V. Machine learning methods for estimation the indicators of phosphogypsum influence in soil. Journal of Soils and Sediments 19, 2265–2276 (2019).

10.Pukalchik, M., Kydralieva, K., Yakimenko, O., Fedoseeva, E. & Terekhova, V. Outlining the Potential Role of Humic Products in Modifying Biological Properties of the Soil—A Review. Frontiers in Environmental Science 7, (2019).

11.Yakimenko, O. S., Terekhova, V. A., Pukalchik, M. A., Gorlenko, M. V. & Popov, A. I. Comparison of Two Integrated Biotic Indices in Assessing the Effects of Humic Products in a Model Experiment. Eurasian Soil Science 52, 736–746 (2019).

12.Fedoseeva, E. V., Patsaeva, S. V., Khundzhua, D. A., Pukalchik, M. A. & Terekhova, V. A. Effect of Exogenic Humic Substances on Various Growth Endpoints of Alternaria alternata and Trichoderma harzianum in the Experimental Conditions. Waste and Biomass Valorization (2020) doi:10.1007/s12649-020-00974-x.

13.Gasanov, M. et al. Sensitivity Analysis of Soil Parameters in Crop Model Supported with High-Throughput Computing. in Computational Science – ICCS 2020 (eds. Krzhizhanovskaya, V. V. et al.) vol. 12143 731–741 (Springer International Publishing, 2020).

14.Matvienko, I. et al. Bayesian aggregation improves traditional single image crop classification approaches. arXiv:2004.03468 [cs, eess] (2020).

15.Shadrin, D. et al. Kalman Filtering for Accurate and Fast Plant Growth Dynamics Assessment. in 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) 1–6 (IEEE, 2020). doi:10.1109/I2MTC43012.2020.9129053.

16.Shadrin, D., Pukalchik, M., Kovaleva, E. & Fedorov, M. Artificial intelligence models to predict acute phytotoxicity in petroleum contaminated soils. Ecotoxicology and Environmental Safety 194, 110410 (2020).

17.Shadrin, D., Pukalchik, M., Uryasheva, A., Rodichenko, N. & Tsetserukou, D. Hyper-spectral NIR and MIR data and optimal wavebands for detecting of apple trees diseases. arXiv:2004.02325 [cs] (2020).

18.Effect of organic substances on wheat (Triticum spp.) productivity and soil enzyme functional stability under drought stress conditions. Res. on Crops 21, (2020).

19.Nesteruk, S. et al. Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case. IEEE Sensors J. 1–1 (2021) doi:10.1109/JSEN.2021.3050084.

20.Shadrin, D. et al. An Automated Approach to Groundwater Quality Monitoring—Geospatial Mapping Based on Combined Application of Gaussian Process Regression and Bayesian Information Criterion. Water 13, 400 (2021).

21.Yudina, E. et al. Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case. Water 13, 888 (2021).

 

 

ALUMNI (MS students)

  • Diana Koldasbaeva (CLS) 2019/2021
  • Dmitriy Vypirailenko (CDISE) 2019/2021
  • Elisaveta Kiseleva (CDISE) 2019/2021
  • Elisaveta Yudina (CDISE) 2018/2020