인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2026.6
- 수록면
- 26 - 37 (12page)
이용수
초록· 키워드
This study aims to predict tractive efficiency using tractor operational data and to analyze field-scale spatial variability by spatially mapping the prediction results from regression analysis and machine learning models. Field experiments were conducted with a 78 kW-class agricultural tractor equipped with a chisel plow, collecting data on engine speed, tillage depth, travel speed, and slip ratio as input variables for predicting tractive efficiency. All sensor data were synchronized with positional information to facilitate spatial analysis. We developed multiple linear regression, support vector regression, and random forest (RF) models to predict tractor tractive efficiency, evaluating model performance using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute percentage error (MAPE). The predicted results were visualized through GIS-based spatial mapping. The regression model explained approximately 47.3% of the variation in tractive efficiency, while the RF model demonstrated the best predictive performance on the test set (R² = 0.771, RMSE = 1.29%, MAPE = 1.16%). The spatial mapping results showed that the machine learning models effectively captured localized spatial variability within the field, surpassing the linear regression model. This highlights their potential as practical tools for identifying low-efficiency zones and supporting decision-making in precision agriculture.
#Tractor(트랙터)
#Chisel Plow(치즐 쟁기)
#Tractive Efficiency(견인 효율)
#Machine Learning(머신러닝)
#Spatial Mapping(공간 매핑)
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목차
- Abstract
- 1. 서론
- 2. 재료 및 방법
- 3. 결과 및 고찰
- 4. 결론
- References
참고문헌
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