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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. 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, br. 1, 2024, str. 108-114. https://doi.org/10.18047/poljo.30.1.14

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

Radočaj, D., Plaščak, I. 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), str. 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]. 30.06.2024. [pristupljeno 13.07.2024.];30(1):108-114. doi: 10.18047/poljo.30.1.14

D. Radočaj, I. Plaščak i M. Jurišić, "A Machine-Learning Approach for the Assessment of Quantitative Changes in the Tractor Diesel-Engine Oil During Exploitation", Poljoprivreda, vol. 30, br. 1, str. 108-114, Lipanj 2024. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:151:030649. [Citirano: 13.07.2024.]

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Podaci o radu
Naslov (engleski)A Machine-Learning Approach for the Assessment of Quantitative Changes in the Tractor Diesel-Engine Oil During Exploitation
AutorDorijan Radočaj
AutorIvan Plaščak
AutorMladen Jurišić
Autorova ustanovaSveučilište Josipa Jurja Strossmayera u Osijeku
Fakultet agrobiotehničkih znanosti Osijek
(Zavod za poljoprivrednu tehniku i obnovljive izvore energije)
Znanstveno / umjetničko
područje, polje i grana		BIOTEHNIČKE ZNANOSTI
Poljoprivreda (agronomija)
Poljoprivredna tehnika i tehnologija
Sažetak (engleski)
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.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - domaća recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaPoljoprivreda
Brojčani podacivol. 30, br. 1, str. 108-114
p-ISSN1330-7142
e-ISSN1848-8080
DOIhttps://doi.org/10.18047/poljo.30.1.14
URN:NBNurn:nbn:hr:151:030649
Datum objave publikacije2024-06-30
URL dokumentahttps://hrcak.srce.hr/318042
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Zaštićeno autorskim pravom.
Datum i vrijeme pohrane2024-06-20 11:11:50