Implementation of XGBoost Models for Predicting CO<sub>2</sub> Emission and Specific Tractor Fuel Consumption
Tillage is one of the most energy-intensive operations in crop production, leading to high fuel consumption and the emission of harmful gases such as CO<sub>2</sub> and NO<sub>x</sub>. This study was conducted under real field conditions to explore how soil parameters influen...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/11/1209 |
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| author | Nebojša Balać Zoran Mileusnić Aleksandra Dragičević Mihailo Milanović Andrija Rajković Rajko Miodragović Olivera Ećim-Đurić |
| author_facet | Nebojša Balać Zoran Mileusnić Aleksandra Dragičević Mihailo Milanović Andrija Rajković Rajko Miodragović Olivera Ećim-Đurić |
| author_sort | Nebojša Balać |
| collection | DOAJ |
| description | Tillage is one of the most energy-intensive operations in crop production, leading to high fuel consumption and the emission of harmful gases such as CO<sub>2</sub> and NO<sub>x</sub>. This study was conducted under real field conditions to explore how soil parameters influence variations in fuel use and exhaust emissions. A machine learning approach based on the XGBoost algorithm was applied to develop predictive models for CO<sub>2</sub> concentrations in exhaust gases and specific fuel consumption. The CO<sub>2</sub> prediction model achieved an accuracy exceeding 80%, while the model for fuel consumption reached over 65%. Although not optimized for high precision, these models offer a valuable basis for preliminary assessments and highlight the potential of data-driven approaches for improving energy efficiency and environmental sustainability in agricultural mechanization. |
| format | Article |
| id | doaj-art-eafabbcb41c34878bcf8648cf914e294 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-eafabbcb41c34878bcf8648cf914e2942025-08-20T03:46:49ZengMDPI AGAgriculture2077-04722025-05-011511120910.3390/agriculture15111209Implementation of XGBoost Models for Predicting CO<sub>2</sub> Emission and Specific Tractor Fuel ConsumptionNebojša Balać0Zoran Mileusnić1Aleksandra Dragičević2Mihailo Milanović3Andrija Rajković4Rajko Miodragović5Olivera Ećim-Đurić6Kite d.o.o., 21333 Čenej, SerbiaDepartment of Agricultural Engineering, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade, SerbiaDepartment of Agricultural Engineering, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade, SerbiaDepartment of Agricultural Engineering, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade, SerbiaDepartment of Agricultural Engineering, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade, SerbiaDepartment of Agricultural Engineering, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade, SerbiaDepartment of Agricultural Engineering, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade, SerbiaTillage is one of the most energy-intensive operations in crop production, leading to high fuel consumption and the emission of harmful gases such as CO<sub>2</sub> and NO<sub>x</sub>. This study was conducted under real field conditions to explore how soil parameters influence variations in fuel use and exhaust emissions. A machine learning approach based on the XGBoost algorithm was applied to develop predictive models for CO<sub>2</sub> concentrations in exhaust gases and specific fuel consumption. The CO<sub>2</sub> prediction model achieved an accuracy exceeding 80%, while the model for fuel consumption reached over 65%. Although not optimized for high precision, these models offer a valuable basis for preliminary assessments and highlight the potential of data-driven approaches for improving energy efficiency and environmental sustainability in agricultural mechanization.https://www.mdpi.com/2077-0472/15/11/1209tractor exhausts emissionpredictive soil tillageVRA soil tillageprecision agriculturemachine learningXGBoost model |
| spellingShingle | Nebojša Balać Zoran Mileusnić Aleksandra Dragičević Mihailo Milanović Andrija Rajković Rajko Miodragović Olivera Ećim-Đurić Implementation of XGBoost Models for Predicting CO<sub>2</sub> Emission and Specific Tractor Fuel Consumption Agriculture tractor exhausts emission predictive soil tillage VRA soil tillage precision agriculture machine learning XGBoost model |
| title | Implementation of XGBoost Models for Predicting CO<sub>2</sub> Emission and Specific Tractor Fuel Consumption |
| title_full | Implementation of XGBoost Models for Predicting CO<sub>2</sub> Emission and Specific Tractor Fuel Consumption |
| title_fullStr | Implementation of XGBoost Models for Predicting CO<sub>2</sub> Emission and Specific Tractor Fuel Consumption |
| title_full_unstemmed | Implementation of XGBoost Models for Predicting CO<sub>2</sub> Emission and Specific Tractor Fuel Consumption |
| title_short | Implementation of XGBoost Models for Predicting CO<sub>2</sub> Emission and Specific Tractor Fuel Consumption |
| title_sort | implementation of xgboost models for predicting co sub 2 sub emission and specific tractor fuel consumption |
| topic | tractor exhausts emission predictive soil tillage VRA soil tillage precision agriculture machine learning XGBoost model |
| url | https://www.mdpi.com/2077-0472/15/11/1209 |
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