TECHNOLOGY FOR WASHING AGRICULTURAL MACHINERY AND A METHOD FOR EVALUATING ITS EFFECTIVENESS AT DIFFERENT LEVELS OF CONTAMINATION
DOI:
https://doi.org/10.32718/agroengineering2025.29.34-47Keywords:
washing, agricultural machinery, efficiency, algorithm, method, pollution, cyber-physical systemAbstract
An analysis of existing technologies and approaches to washing agricultural machinery has been carried out. It has been established that, despite their prevalence, they focus mainly on individual performance indicators and do not take into account the complex impact of time, energy consumption, and the quality of wash water. The feasibility of improving the technology for washing agricultural machinery and developing a method that allows for an objective assessment of the process effectiveness at different levels of contamination has been substantiated. The improved technology for washing agricultural machinery is based on a combination of traditional operations with modern intelligent approaches to quality control. The algorithm of this technology includes 18 steps that ensure the use of machine vision for the initial assessment of the level of contamination and the adaptation of the sequence of operations to the actual condition of the surfaces. Additional washing is provided to remove difficult stains and re-control after basic cleaning, which forms a closed cycle of the process, taking into account sensor data, reducing resource consumption and improving environmental safety. The developed method for evaluating washing efficiency consists of six blocks and provides a comprehensive approach to performance analysis, combining surface cleaning criteria, wash water quality, operation duration, and resource consumption into an integrated index. This allows for an objective comparison of washing modes, taking into account technological and economic aspects, and forms the basis for the implementation of automated control systems. As a result of the research, the washing efficiency of a John Deere 6130R tractor with a Horsch Pronto 6 DC trailer seed drill was evaluated at three levels of contamination. It was found that at a low level, the integral efficiency index reached the highest values due to the optimal ratio of cleaning quality and resource consumption. At the same time, at a high level of contamination, the key factors reducing efficiency are excessive water and energy consumption, as well as increased cycle duration. Further research should be focused on the development of adaptive cyber-physical washing systems capable of optimizing operating modes in real time, taking into account the actual level of contamination, resource consumption, and environmental requirements.
References
Batyuk, B., & Dyndyn, M. (2020). Coordination of configurations of complex organizational and technical systems for development of agricultural sector branches. Journal of Automation and Information Sciences, 2(2), 63–76.
Boe, T., Calfee, W., Lemieux, P., Serre, S., Abdel-Hady, A., Monge, M., & Howard, J. (2023). Evaluation of cleaning and disinfection protocols for commercial farm equipment following a foreign animal disease outbreak. Remediation Journal, 33(3), e21762. https://doi.org/10.1002/rem.21762.
DeCooman, C. (2023). Prioritize safety when using pressure washers. AgProud. Retrieved from: https://www.agproud.com/articles/58447-prioritize-safety-when-using-pressure-washers.
Díaz-Rodríguez, A. M., Parra Cota, F. I., Cira Chávez, L. A., García Ortega, L. F., Estrada Alvarado, M. I., Santoyo, G., & de los Santos-Villalobos, S. (2025). Microbial inoculants in sustainable agriculture: Advancements, challenges, and future directions. Plants, 14 (2), 191. https://doi.org/10.3390/plants14020191.
Flores, T. K. S., Magalhães, L. A. F., Cortez, P. C., & Brito, A. H. (2021). Adaptive pressure control system based on the maximum power transfer theorem for water supply systems. Sensors, 21(3), 5156. https://doi.org/10.3390/s21155156.
Hütten, N., Alves Gomes, M., Hölken, F., Andricevic, K., Meyes, R., & Meisen, T. (2024). Deep learning for automated visual inspection in manufacturing and maintenance: A survey of open-access papers. Applied System Innovation, 7(1), 11. https://doi.org/10.3390/asi7010011.
International Organization for Standardization (2016). ISO 7027-1:2016. Water quality – Determination of turbidity – Part 1: Quantitative methods. Geneva: ISO.
Jensen, T. A., Antille, D. L., & Tullberg, J. N. (2025). Improving on-farm energy use efficiency by optimizing machinery operations and management: A review. Agricultural Research, 14, 15–33. https://doi.org/10.1007/s40003-024-00824-5.
Jiang, L., Xu, B., Husnain, N., & Wang, Q. (2025). Overview of agricultural machinery automation technology for sustainable agriculture. Agronomy, 15(6), 1471. https://doi.org/10.3390/agronomy15061471.
Krklješ, D. B., Kitić, G. V., Petes, C. M., Birgermajer, S. S., Stanojev, J. D., Bajac, B. M. … Janković, N. N. (2023). Multiparameter water quality monitoring system for continuous monitoring of fresh waters. Sensors, 23, 6396. https://doi.org/10.1109/JSEN.2024.3368560.
Krupetskykh, V. P., Domaratskyi, O. O., & Revtio, O. Ya. (2020). Efektyvnist vykorystannia mashynno-traktornykh ahrehativ u roslynnytstvi [Efficiency of the use of machine-tractor aggregates in crop production]. Tavriiskyi naukovyi visnyk, 111, 96–104. https://doi.org/10.32851/2226-0099.2020.111.13.
Kumar, M., Khamis, K., Stevens, R., & Hannah, D. M. (2024). In-situ optical water quality monitoring sensors—applications, challenges, and future opportunities. Frontiers in Water, 6, 1380133. https://doi.org/10.3389/frwa.2024.1380133.
Nasr, G., Abdel Hamid, Z., & Refai, M. (2023). Agricultural machinery corrosion. In Agricultural machinery corrosion. IntechOpen. https://doi.org/10.5772/intechopen.108918
Pordesimo, L. O., Wilkerson, E. G., Womac, A. R., & Cutter, C. N. (2002). Process engineering variables in the spray washing of meat and produce. Journal of Food Protection, 65 (1), 222–237. https://doi.org/10.4315/0362-028X-65.1.222.
Stubenrauch, C., & Drenckhan, W. (2024). Cleaning solid surfaces with liquid interfaces and foams: From theory to applications. Current Opinion in Colloid & Interface Science, 72, 101818. https://doi.org/10.1016/j.cocis.2024.101818.
Sun, C., Shu, L., Gu, X., Li, D., & Li, W. (2020). Real-time control of urban water cycle under cyber-physical systems: Framework, interoperability and case study in Barcelona. Water, 12(2), 406. https://doi.org/10.3390/w12020406.
Tomašková, M., Sobotova, L., & Matiskova, D. (2019). Machinery fire in agriculture and its impact on the environment. In Proceedings of the 2019 International Council on Technologies of Environmental Protection (ICTEP). https://doi.org/10.1109/ICTEP48662.2019.8968991.
Torrens, A., Sepúlveda-Ruiz, P., Aulinas, M., & Folch, M. (2025). Innovative carwash wastewater treatment and reuse through nature-based solutions. Clean Technologies, 7(1), 12. https://doi.org/10.3390/cleantechnol7010012.
Tryhuba, A., Boyarchuk, V., Tryhuba, I., Ftoma, O., Padyuka, R., & Rudynets, M. (2021). Forecasting the risk of the resource demand for dairy farms basing on machine learning. CEUR Workshop Proceedings, 2631, 327–340.
Tryhuba, A., Koval, N., Tryhuba, I., & Boiarchuk, O. (2022). Application of SARIMA models in information systems forecasting seasonal volumes of food raw materials of procurement on the territory of communities. CEUR Workshop Proceedings, 3295, 64–75.
Tryhuba, A., Tryhuba, I., & Bashynsky, O. (2020). Conceptual model of management of technologically integrated industry development projects. In Proceedings of the 15th International Scientific and Technical Conference on Computer Sciences and Information Technologies (pp. 155–158). https://doi.org/10.1109/CSIT49958.2020.9321903.
Tyrovolas, M., & Hajnal, T. (2021). Inter-communication between programmable logic controllers using IoT technologies: A Modbus RTU/MQTT approach. arXiv preprint arXiv:2102.05988. https://doi.org/10.48550/arXiv.2102.05988.
