GIS-based modeling of spatially explicit agricultural machinery movement in precision agriculture systems

Vitaliy Zatserkovnyi; Victor Vorokh; Olexandr Nikolaienko; Tetiana Pastushenko; Iryna Siuiva

doi:10.17721/1728-2713.113.12

Authors

Vitaliy Zatserkovnyi Taras Shevchenko National University of Kyiv https://orcid.org/0009-0007-3090-6988
Victor Vorokh Taras Shevchenko National University of Kyiv https://orcid.org/0009-0005-0112-8422
Olexandr Nikolaienko National University "Kyiv Aviation Institute" https://orcid.org/0000-0002-8895-5412
Tetiana Pastushenko Taras Shevchenko National University of Kyiv
Iryna Siuiva Taras Shevchenko National University of Kyiv https://orcid.org/0000-0002-5001-2750

DOI:

https://doi.org/10.17721/1728-2713.113.12

Keywords:

precision agriculture, Global Navigation Satellite System (GNSS), geographic information systems (GIS), machinery movement trajectory, spatial-temporal analysis

Abstract

Background. The development of precision agriculture technologies is accompanied by a rapid increase in the volume of spatial-temporal data that characterizes not only the state of agroecosystems but also the operational parameters of agricultural machinery. In this context, the use of Global Navigation Satellite System (GNSS) data for analyzing machinery movement regimes within individual fields becomes particularly important.

Methods. An approach to the spatial-temporal analysis of agricultural machinery movement based on GNSS trajectories using geographic information systems is presented. The study relies on representing the movement trajectory as a set of elementary spatial-temporal segments for which metric, temporal, and speed characteristics are determined. This makes it possible to move from purely temporal or average indicators to a spatially oriented interpretation of movement parameters.

Results. The study was conducted using a production agricultural field located in the western region of Ukraine as a case study. Based on GNSS data, machinery movement trajectories were reconstructed, segmented, and average movement speeds were calculated for individual segments. The results were visualized in the form of thematic maps and graphs that reflect the spatial variability of speed regimes within the field. The analysis revealed pronounced within-field heterogeneity in machinery speed, caused by field configuration, the organization of passes, and maneuvering zones. The spatial representation of speed parameters made it possible to identify and visualize zones of stable operation, deceleration, and turning, which are not always detectable through tabular or purely temporal data analysis.

Conclusions. The obtained results confirm the feasibility of using segmented spatial-temporal analysis of GNSS trajectories to study the movement of agricultural machinery and substantiate the applicability of this approach in precision agriculture technologies for evaluating the efficiency of field operations and supporting management decision-making.

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