Navigation data obtained during agricultural machinery operation provide valuable information for analyzing field processes and evaluating the efficiency of harvesting operations. This study investigates the operating speed modes of a grain harvester using GIS and GNSS monitoring data. The research was conducted on a 117 ha production field located in the Ternopil region of Ukraine. GNSS observations were processed in the ArcGIS geographic information system to reconstruct harvester movement trajectories and classify them according to predefined speed intervals. For each speed class, the number of passes and the total trajectory length were calculated. The results revealed an uneven distribution of harvester passes among the speed classes. The dominant operating mode was identified within the speed range of 9.1–11.4 km/h, which accounted for the highest number of passes and the greatest total trajectory length. The developed cartographic models illustrate the spatial distribution of harvester speed and operational characteristics across the study field. The integration of GNSS monitoring with GIS-based spatial analysis provides an effective approach for assessing harvesting machinery performance and supporting precision agriculture applications.
Bettucci, F., Lindia, P., Trunfio, P., & Sartori, L. (2026). Operational state classification of agricultural machinery using GNSS data: A minimal-input approach for field efficiency assessment. Computers and Electronics in Agriculture, 240, 111193. https://doi.org/10.1016/j.compag.2025.111193
Chen, Y., Li, G., Zhou, K., & Wu, C. (2023). Field–Road Operation Classification of Agricultural Machine GNSS Trajectories Using Spatio-Temporal Neural Network. Agronomy, 13(5), 1415. https://doi.org/10.3390/agronomy13051415
Guo, J., Li, X., Li, Z., Hu, L., Yang, G., Zhao, C., Fairbairn, D., Watson, D., & Ge, M. (2018). Multi-GNSS precise point positioning for precision agriculture. Precision Agriculture, 19(5), 895–911. https://doi.org/10.1007/s11119-018-9563-8
Radočaj, D., Jurišić, M., & Gašparović, M. (2023). Global navigation satellite systems as state-of-the-art solutions in precision agriculture. Agriculture, 13(7), 1417. https://doi.org/10.3390/agriculture13071417
Zatserkovnyi, V. I., Vorokh, V. V., Hloba, O. M., Mironchuk, T. A., & Plichko, L. V. (2025). Features of GIS, GPS, remote sensing, and artificial intelligence application in soil research. Visnyk of Taras Shevchenko National University of Kyiv. Geology, 110. https://doi.org/10.17721/1728-2713.110.11