Publications

Challenges in data-driven geospatial modeling for environmental research and practice

Published in Paper in Nature Communications, 2024

Machine learning-based geospatial applications offer unique opportunities for environmental monitoring due to domains and scales adaptability and computational efficiency

Recommended citation: Koldasbayeva, D., Tregubova, P., Gasanov, M. et al. Challenges in data-driven geospatial modeling for environmental research and practice. Nat Commun 15, 10700 (2024). https://doi.org/10.1038/s41467-024-55240-8 https://www.nature.com/articles/s41467-024-55240-8

A New Multi-objective Approach to Optimize Irrigation Using a Crop Simulation Model and Weather History

Published in Paper presented at the ICCS 2021, 2021

Optimization of water consumption in agriculture is necessary to preserve freshwater reserves and reduce the environment’s burden.

Recommended citation: Gasanov M., et al, 2021 https://link.springer.com/chapter/10.1007/978-3-030-77970-2_7

Sensitivity Analysis of Soil Parameters in Crop Model Supported with High-Throughput Computing

Published in Paper presented at the ICCS 2020, 2020

In this research, we perform a sensitivity analysis of soil parameters which play an essential role in plant growth for the MONICA agro-ecosystem model. We utilize Sobol sensitivity indices to estimate the importance of main soil parameters for several crop cultures (soybeans, sugar beet and spring barley).

Recommended citation: Gasanov M., et al, 2020 https://link.springer.com/chapter/10.1007%2F978-3-030-50436-6_54

Effect of Lignohumate on Soil Biological Activity on the Bali Island, Indonesia

Published in Paper in Eurasian Soil Science, 2020

Interest in studying the effect of commercial humic products (HPs) on the biological activity of soils is dictated by the environmental safety of their use (HPs are produced from natural raw materials), low market value, and high efficiency.

Recommended citation: Pozdnyakov L. A. et al., 2020 https://link.springer.com/article/10.1134/S1064229320050117

Bayesian aggregation improves traditional single image crop classification approaches

Published in Paper presented at the ICLR 2020 Workshop on Computer Vision for Agriculture (CV4A), 2020

Machine learning (ML) methods and neural networks (NN) are widely implemented for crop types recognition and classification based on satellite images.

Recommended citation: Matvienko I., et al, 2020 https://arxiv.org/abs/2004.03468