Vol. 337 No. 5 (2026)
DOI https://doi.org/10.18799/24131830/2026/5/5368
Maize yield forecasting with machine learning methods based on the geoinformation analysis results of soil hardness
Relevance. The topic of precision farming based on the use of geoinformation technologies is developing every year in new approaches to applied research. Increasing the efficiency of agricultural production is a key task of the state policy of the Russian Federation. This study analyzes such a soil indicator as hardness, and on the basis of experimental work, the effect of this indicator on the yield of such an agricultural crop as maize is revealed. In 2024, compared with 2001, an expansion of maize sown areas by 4.0 times (by 1,960.5 thousand hectares) is noted. Aim. Application of geoinformation technologies and machine learning to assess the possible impact of soil hardness on maize yield. Objects. 3D geospatial data in precision agriculture. Methods. Clustering, machine learning, ensemble, bayesian optimization methods. Results. The authors have analyzed the advantages of using geoinformation systems and spatial analysis tools for agricultural processes. The paper considers the effect of the soil hardness parameter on the yield of agricultural crops. Based on the data collected by the penetrometer, the authors formed the database. According to the results of the database analysis, using clustering methods, spatial models were created. The models display zones by average values of soil hardness. Based on the comparative analysis, the authors created a model for predicting maize yields based on soil hardness data. Based on this model, the authors selected optimal soil hardness values for each of the 60-centimeter intervals.
For citation: Kolesnikov A.A., Yakovlev D.A., Poshivaylo Ya.G., Ivanov N.M., Kostić M., Bricheva A.V., Bambukh D.V. Maize yield forecasting with machine learning methods based on the results of soil hardness geoinformation analysis. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 2026, vol. 337, no. 5, pp. 21-35. https://doi.org/10.18799/24131830/2026/5/5368
Keywords:
soil hardness, forecasting maize yield, precision farming, geographic information analysis, interpolation, clustering, heat map, machine learning
References:
1. Karmanov A.G., Knyshev A.I., Eliseeva V.V. Geoinformation systems of territorial management. St Petersburg, ITMO University Publ., 2015. 121 p. (In Russ.)
2. Bajic D., Gnjato R., Trbic G., Adzic D., Gnjato S., Todorovic S., Lukic N. Geoinformation systems and precision agriculture: concept, theory and practice. Geopolitics and ecogeodynamics of regions, 2019, vol. 5 (15), no. 2, pp. 51–64. (In Russ.)
3. Rulev A.S., Shinkarenko S.S., Bodrova V.N., Sidorova N.V. Geoinformation technologies in precision farming. Izvestia of the Lower Volga Agro-University Complex, 2018, vol. 4 (52), pp. 115–122. (In Russ.) DOI: 10.32786/2071-9485-2018-04-15.
4. Rimpika, Anushi, Manasa S., Anusha K.N., Sharma S., Thakur A., Shilpa, Sood A. An overview of precision farming. International Journal of Environment and Climate Change, 2023, vol. 13, no. 12, pp. 441–456. DOI: 10.9734/ijecc/2023/v13i123701.
5. Medvedev V.V. Soil hardness. Kharkov, KG1 "City Printing House", 2009. 152 p. (In Russ.)
6. Biberdzic M., Barac S., Lalevic D., Вjikic A., Prodanovic D., Rajicic V. Influence of soil tillage system on soil compaction and winter wheat yield. Chilean journal of agricultural research, 2020. vol. 80, no. 1, pp. 80–89. DOI: 10.4067/S0718-58392020000100080.
7. Ferreira C.J.B., Tormena C.A., Severiano E.Da.C., Zotarelli L., Betioli Júnior E. Soil compaction influences soil physical quality and soybean yield under long-term no-tillage. Archives of Agronomy and Soil Science, 2021, vol. 67, no. 3, pp. 383–396. DOI: 10.1080/03650340.2020.1733535.
8. Shinoto Y., Otani R., Matsunami T., S. Maruyama S. Analysis of the shallow root system of maize grown by plowing upland fields converted from paddy fields: effects of soil hardness and fertilization. Plant Production Science, 2021, vol. 24, no. 3, pp. 297–305. DOI: 10.1080/1343943X.2020.1863823.
9. Yakovlev N.S., Sineshchekov V.E., Markin V.V. Analysis of systems of autumn tillage of soil for grain crops. Vestnik NGIEI, 2021, no. 4 (119), pp. 5–20. (In Russ.) DOI: 10.24412/2227-9407-2021-4-5-20.
10. Mileusnić Z.I., Saljnikov E., Radojević R.L., Petrović D.V. Soil compaction due to agricultural machinery impact. Journal of Terramechanics, 2022, vol. 100, pp. 51–60. DOI: 10.1016/j.jterra.2021.12.002.
11. Shaheb M.R., Venkatesh R., Shearer S.A. A review on the effect of soil compaction and its management for sustainable crop production. Journal of Biosystems Engineering, 2021, vol. 46, pp. 417–439. DOI: 10.1007/s42853-021-00117-7.
12. Shchitov S.V., Krivutsa Z.F., Polikutina E.S., Panova E.V., Surin R.O. Results of research of a wheeled semi-frame power vehicle with a frontally mounted piercer-slot cutter. The Bulletin of Izhevsk State Agricultural Academy, 2024, no. 4 (80), pp. 160–166. (In Russ.) DOI: 10.48012/1817-5457_2024_4_160-166.
13. Panasyuk A.N., Epifantsev V.V., Degtyarev D.A. Limitations of the use of wheeled tractors in crop production technologies by compaction effect (in the conditions of the Amur region). Far Eastern Agricultural Journal, 2024, vol. 18, no. 1, pp. 83–90. (In Russ.) DOI: 10.22450/1999-6837-2024-18-1-83-90.
14. Schjønning P., Lamandé M., Munkholm L., Lyngvig H., Nielsen J. Soil precompression stress, penetration resistance and crop yields in relation to differently-trafficked, temperate-region sandy loam soils. Soil and Tillage Research, 2016, vol. 163, pp. 298–308. DOI: 10.1016/j.still.2016.07.003.
15. Khanal S., Fulton J., Klopfenstein A., Douridas N., Shearer S. Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield. Computers and electronics in agriculture, 2018, vol. 153. pp. 213–225. DOI: 10.1016/j.compag.2018.07.016
16. Kinoshita R., Tani M., Sherpa S., Ghahramani A., Es H. Soil sensing and machine learning reveal factors affecting maize yield in the mid‐Atlantic United States. Agronomy Journal, 2023, vol. 115.1, pp. 181–196. DOI: 10.1002/agj2.21223
17. SS R 17.4.4.02-2017. Nature protection. Soils. Methods of sampling and sample preparation for chemical, bacteriological, helminthological analysis. Moscow, Standartinform Publ., 2018. 16 p. (In Russ.)
18. SS R 58586-2019. Selection and preparation of soil samples for isotope analysis. Moscow, Standartinform Publ., 2020. 12 p. (In Russ.)
19. SS 20915-2011. Testing of agricultural machinery. Methods for determining test conditions. Moscow, Standartinform Publ., 2013. 28 p. (In Russ.)
20. Yakovlev N.S., Nazarov N.N., Chernykh V.I. Hardness of soil and soil aggregates. Agrarian science – for agriculture: collection of articles. Barnaul, February 7–8, 2017. Barnaul, Altay State University Publ., 2017. B. 3, pp. 60–62.
21. Koolen A.J. A method for soil compactibility determination. Journal of Agricultural Engineering Research, 1974, vol. 19, no. 3, pp. 271–278.
22. Dzhabborov N.I., Dobrinov A.V. Optimal design of tillage machines taking into account their required power. AgroEcoEngineering, 2021, no. 1 (106), pp. 50–62. (In Russ.) DOI: 10.24411/2713-2641-2021-10277.
23. Khutuev A.M., Zanilov A.Kh., Tutukova D.A., Savin I.Yu. NDVI of crops as a remote indicator of the quality of arable soils. Dokuchaev Soil Bulletin, 2024, no. 121, pp. 70–85. (In Russ.) DOI: 10.19047/0136-1694-2024-121-70-85.
24. Rakhmonov I.U., Niyozov N.N., Ushakov V.Ya. Forecasting electricity consumption based on the principal component analysis (PCA). Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 2024, vol. 335, no 12. p. 198–209. DOI: 10.18799/24131830/2024/12/4731.
25. Kochegurov A.I., Denisov V.I., Zadorozhnykh E.A. Analysis of the application of machine learning methods in problems of rock classification using core samples. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 2024, vol. 335, no. 9, pp. 148–159. DOI: 10.18799/24131830/2024/9/4792.
