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...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/11/1209 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2077-0472 |