Scientific publications

@inproceedings{Sedys2026,

title = {A Case Study of Detecting Nutrient Deficiencies in Corn Using Multispectral Satellite Imagery},

author = {Gintautas \v{S}edys and Mantas Luko\v{s}evi\v{c}ius and Ernestas Petrauskas and Art\={u}ras Gotceitas and Ernestas Zaleckas and Martynas Grei\v{c}ius},

editor = {Audrius Lopata and Daina Gudonien\.{e} and Jonas \v{C}eponis},

url = {https://mantas.info/get-publication/?f=Detecting_nutrient_deficiencies_in_corn_using_multispectral_satellite_imagery.pdf

https://link.springer.com/chapter/10.1007/978-3-032-16808-5_2},

doi = {10.1007/978-3-032-16808-5_2},

isbn = {978-3-032-16808-5},

year  = {2026},

date = {2026-04-01},

urldate = {2026-04-01},

booktitle = { Information and Software Technologies. ICIST 2025},

pages = {15\textendash26},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {We explore the feasibility of detecting nitrogen deficiencies in a cornfield using exclusively multispectral satellite imagery. Attempts to leverage Sentinel-1 and Sentinel-2 data demonstrate that while nutrient shortages are successfully identified through multi-sensor approaches\textemdash often incorporating UAV imagery, ground-based measurements, or advanced vegetation indices\textemdashreliably detecting these deficits from satellite imagery alone remains challenging. Data scarcity, uneven initial and structural soil conditions, noise introduced by atmospheric effects and cloud cover complicate the direct association of spectral signals with nutrient levels. Despite observed differences in key vegetation indices, such as NDVI, between fertilized and unfertilized plots at late growth stages, early-season detection proved unreliable. These findings illustrate the complexity of detecting nutrient deficiencies and highlight the need for improved data quality, supplementary ground truth measurements, or more sophisticated modeling approaches to enhance the accuracy and accessibility of satellite-only nutrient deficiency detection in precision agriculture.},

keywords = {Geospatial, Precision agriculture},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{BukauskasLukosevicius20,

title = {Accident localization at the district heating network of Kaunas region using machine learning},

author = {Mantas Bukauskas and Mantas Luko\v{s}evi\v{c}ius},

url = {https://mantas.info/get-publication/?f=heating_accident_localization.pdf

http://ceur-ws.org/Vol-2698/p18.pdf},

isbn = {1613-0073},

year  = {2020},

date = {2020-04-23},

booktitle = {Proceedings of the IVUS 2020},

volume = {2698},

pages = {128-137},

publisher = {CEUR},

address = {Kaunas, Lithuania},

keywords = {Geospatial},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{CiurlionisLukosevicius18,

title = {Nowcasting precipitation using weather radar data for Lithuania: the first results},

author = {Aivaras \v{C}iurlionis and Mantas Luko\v{s}evi\v{c}ius},

url = {https://mantas.info/get-publication/?f=nowcasting_LT_weather_1st_results.pdf

http://ceur-ws.org/Vol-2147/p10.pdf},

year  = {2018},

date = {2018-05-28},

urldate = {2018-05-28},

booktitle = {SYSTEM 2018 Symposium for Young Scientists in Technology, Engineering and Mathematics},

volume = {2147},

pages = {55-60},

publisher = {CEUR},

keywords = {Convolutional neural networks, Deep learning, Geospatial},

pubstate = {published},

tppubtype = {inproceedings}

}