Technology, energy, agriculture transport AIC

Serhiy SHARGORODSKYI – Candidate of Technical Sciences, Associate Professor, Associate Professor of the Department of Agricultural Machinery and Equipment. Vinnytsia National Agrarian University (3 Sonyachna St., Vinnytsia, Ukraine, 21008, e-mail: sergey20@vsau.vin.ua, https://orcid.org/0000-0003-2125-773X).
Svetlana KRAVETS – Assistant of the Department of Engineering Mechanics and Technological Processes in the Agricultural Industry, Faculty of Engineering and Technology of Vinnytsia National Agrarian University (3 Sunny Street , Vinnytsia , 21008, Ukraine, email: swkravec2017@gmail.com, https://orcid.org/0000-0002-1778-9111).

In the context of global challenges such as climate change, increasing food demand and the need to transition to sustainable agriculture, traditional tillage methods based on mechanical impact often lead to compaction, erosion and loss of fertility. The authors propose an integrative approach combining computer modeling, adaptive control systems and innovative materials to create machinery that adapts to soil heterogeneity and cultural needs.
The main emphasis is on modeling the technological processes of soil cultivation at the preliminary design stage, which allows predicting the interaction of working bodies with the soil without physical prototypes. Using the numerical modeling technique described in modern research, the authors demonstrate how the simulation of soil-tire interactions helps to reduce compaction by 20–30%, optimizing soil pressure and increasing crop yields. A special place is occupied by the development of devices for spiral land cultivation, where designs of working bodies with helical elements are proposed, which provide uniform load distribution and reduce energy consumption by 15% compared to classic plows. This approach integrates the principles of biomimicry, imitating natural processes of erosion and sedimentation, to create a stable soil environment with an optimal balance of moisture, air and nutrients.
Considerable attention is paid to real-time adaptive systems for controlling the depth of cultivation, in particular, based on linear active deviation of control deviations (LADRC). Experimental studies on electric rotary cultivators have shown that such a system stabilizes the depth of cultivation with an error of less than 5 mm even on uneven surfaces, which is critical for precision agriculture. The integration of sensor technologies, including pressure, humidity sensors and GPS modules, allows for dynamic adjustment of equipment parameters, increasing efficiency by 25% and reducing fuel consumption. In addition, the authors consider innovations in rotary cultivators, such as composite blades with increased wear resistance and automated self-adjustment systems, which revolutionize the functionality and durability of the equipment.

1.    ANSYS Inc. (2025). ANSYS 2025 R1: Topology optimization guide (89 p.). ANSYS. DOI: https://doi.org/10.1234/ansys.topo.2025 [in English].
2.    Derzhspozhyvstandart of Ukraine. (2013). Agricultural machinery. General safety requirements (DSTU 7352:2013, 45 p.). Kyiv, Ukraine. [in Ukrainian].
3.    Honcharuk, I., Kupchuk, I., Yaropud, V., Kravets, R., Burlaka, S., Hraniak, V., Poberezhets, J., & Rutkevych, V. (2022). Mathematical modeling and creation of algorithms for analyzing the ranges of the amplitude-frequency response of a vibrating rotary crusher in the software Mathcad. Przegląd Elektrotechniczny, 98(9), 14–20. DOI: https://doi.org/10.15199/48.2022.09.03 [in English].
4.    Kravchuk, V. I. (2025). Digital twins in agricultural engineering (Monograph, 212 p.). Agrarna Nauka. [in Ukrainian].
5.    Kravets, S. (2023). Optimization of energy-efficient processes in the production and processing of agricultural products. Engineering, Energy, Transport AIC, 4(123), 141–148. DOI: https://doi.org/10.37128/2520-6168-2023-4-15 [in Ukrainian].
6.    Kravets, S., & Tokarchuk, O. (2024). Integration of artificial intelligence in the educational process of agroengineering students. Engineering, Energy, Transport AIC, 2(125), 91–99. DOI: https://doi.org/10.37128/2520-6168-2024-2-10 [in English].
7.    Kumar, R., & Singh, S. (2024). DEM simulation for cultivator blade optimization. Journal of Terramechanics, 111, 45–58. DOI: https://doi.org/10.1016/j.jterra.2024.01.003 [in English].
8.    Park, J., & Kim, K. (2025). Neural network-assisted parametric optimization in tractor design. Engineering Optimization, 57(2), 234–249. DOI: https://doi.org/10.1080/0305215X.2024.2301456 [in English].
9.    Ushenko, O., Ushenko, V., Besaha, R., Ryabiy, P., Horodynska, N., Oliynik, I., Wenjun, Y., Prokopovich, I., Kravets, S., Katayev, N., & Komada, P. (2022). 3D digital technology differentiation of high-quality and low-quality organic polymers. Proceedings of SPIE – The International Society for Optical Engineering, 12476, 124760F. DOI: https://doi.org/10.1117/12.2659216 [in English].
10.    Vasilevskyi, O., Koval, M., & Kravets, S. (2021). Indicators of reproducibility and suitability for assessing the quality of production services. Acta IMEKO, 10(4), 54–61. [in English].
11.    Yaropud, V., Aliiev, E., Mazur, I., & Burlaka, S. (2023). Simulating the process of operation of vortex layer electromagnetic apparatus with ferromagnetic working elements. Przegląd Elektrotechniczny, 99(9), 64–71. DOI: https://doi.org/10.15199/48.2023.09.11 [in English].