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Scientific paper - Original scientific paper
A Machine-Learning Approach for the Assessment of Quantitative Changes in the Tractor Diesel-Engine Oil During Exploitation
Poljoprivreda, 30 (2024), 1; 108-114. https://doi.org/10.18047/poljo.30.1.14
Radočaj, Dorijan; Plaščak, Ivan; Jurišić, Mladen

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Radočaj, D., Plaščak, I. & Jurišić, M. (2024). A Machine-Learning Approach for the Assessment of Quantitative Changes in the Tractor Diesel-Engine Oil During Exploitation. Poljoprivreda, 30. (1), 108-114. doi: 10.18047/poljo.30.1.14

Radočaj, Dorijan, et al. "A Machine-Learning Approach for the Assessment of Quantitative Changes in the Tractor Diesel-Engine Oil During Exploitation." Poljoprivreda, vol. 30, no. 1, 2024, pp. 108-114. https://doi.org/10.18047/poljo.30.1.14

Radočaj, Dorijan, Ivan Plaščak and Mladen Jurišić. "A Machine-Learning Approach for the Assessment of Quantitative Changes in the Tractor Diesel-Engine Oil During Exploitation." Poljoprivreda 30, no. 1 (2024): 108-114. https://doi.org/10.18047/poljo.30.1.14

Radočaj, D., Plaščak, I. and Jurišić, M. (2024) 'A Machine-Learning Approach for the Assessment of Quantitative Changes in the Tractor Diesel-Engine Oil During Exploitation', Poljoprivreda, 30(1), pp. 108-114. doi: 10.18047/poljo.30.1.14

Radočaj D, Plaščak I, Jurišić M. A Machine-Learning Approach for the Assessment of Quantitative Changes in the Tractor Diesel-Engine Oil During Exploitation. Poljoprivreda [Internet]. 2024 June 30 [cited 2024 July 12];30(1):108-114. doi: 10.18047/poljo.30.1.14

D. Radočaj, I. Plaščak and M. Jurišić, "A Machine-Learning Approach for the Assessment of Quantitative Changes in the Tractor Diesel-Engine Oil During Exploitation", Poljoprivreda, vol. 30, no. 1, pp. 108-114, June 2024. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:151:030649. [Accessed: 12 July 2024]

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Metadata
Title (english)A Machine-Learning Approach for the Assessment of Quantitative Changes in the Tractor Diesel-Engine Oil During Exploitation
AuthorDorijan Radočaj
AuthorIvan Plaščak
AuthorMladen Jurišić
Author's institutionJosip Juraj Strossmayer University of Osijek
Faculty of Agrobiotechnical Sciences Osijek
(Department of Agricultural Engineering and Renewable Energy Resources)
Scientific / art field,
discipline and subdiscipline		BIOTECHNICAL SCIENCES
Agronomy
Agricultural Engineering and Technology
Abstract (english)
To evaluate the potential of a machine-learning approach in the assessment of quantitative changes in the tractor diesel-engine oil during exploitation, this study aspired to propose a machine-learning regression method to reduce the frequency of expensive and time–consuming engine oil sampling. The input engine-oil datasets with fresh engine oil (Samples A) and with the engine oil subsequent to 250 working hours (Samples B) were sampled for twelve elements in a two–year exploitation study at the Belje company. The field data collection was performed having deployed six heavy, four–wheel-drive FENDT 930 Vario agricultural tractors, each monitored for 1,500 working hours, during which an engine oil was sampled every 250 working hours. The evaluated machine-learning prediction methods, based on a tenfold cross–validation, achieved a moderately high prediction accuracy, with a slightly higher coefficient of determination (R2), in the range of 0.51–0.73, for the Samples B, than those in the range of 0.49–0.64 for the Samples A. These results strongly suggest that none of the machine learning methods constantly achieved high prediction accuracy and that the selection of optimal machine-learning models should be mandatory, having also confirmed a high potential of machine-learning methods in the detection of quantitative changes in tractor diesel-engine oil during exploitation.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - domestic
Publication versionPublished version
Journal titlePoljoprivreda
Numberingvol. 30, no. 1, pp. 108-114
p-ISSN1330-7142
e-ISSN1848-8080
DOIhttps://doi.org/10.18047/poljo.30.1.14
URN:NBNurn:nbn:hr:151:030649
Publication2024-06-30
Document URLhttps://hrcak.srce.hr/318042
Type of resourceText
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-06-20 11:11:50