538
Views
1
CrossRef citations to date
0
Altmetric
Mechanical Engineering

Cloud-driven serverless framework for generalised tractor fuel consumption prediction model using machine learning

ORCID Icon, , , , &
Article: 2311810 | Received 07 Dec 2023, Accepted 23 Jan 2024, Published online: 06 Feb 2024

References

  • Abdulrahman, A.-J., Saad, A.-H., & Aboukarima, A. (2010). An excel spreadsheet to estimate performance parameters for chisel plow-tractor combination based on trained an artificial neural network. Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Agriculture, 67(2). https://doi.org/10.15835/buasvmcn-agr:5173
  • Agricultural, A. S. O., & Engineers, B. (2011). Agricultural machinery management data. American Society of Agricultural and Biological Engineers.
  • Ali, M. (2020). PyCaret: An open source, low-code machine learning library in Python. PyCaret Version, 2.
  • Al-Janobi, A., Al-Hamed, S., Aboukarima, A., & Almajhadi, Y. (2020). Modeling of draft and energy requirements of a moldboard plow using artificial neural networks based on two novel variables. Engenharia Agrícola, 40(3), 363–373. https://doi.org/10.1590/1809-4430-eng.agric.v40n3p363-373/2020
  • Almaliki, S., Alimardani, R., & Omid, M. (2016a). Artificial neural network based modeling of tractor performance at different field conditions. Agricultural Engineering International: CIGR Journal, 18(4), 262–274.
  • Almaliki, S., Alimardani, R., & Omid, M. (2016b). Fuel consumption models of MF285 tractor under various field conditions. Agricultural Engineering International: CIGR Journal, 18(3), 147–158.
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  • Cohen, I., Huang, Y., Chen, J., Benesty, J., Benesty, J., Chen, J., Huang, Y., & Cohen, I. (2009). Pearson correlation coefficient. Noise reduction in speech processing, 1–4.
  • Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., & Singer, Y. (2006). Online passive aggressive algorithms.
  • Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle regression.
  • Eisenman, B. (2015). Learning react native: Building native mobile apps with JavaScript. O'Reilly Media.
  • Freund, Y., & Mason, L. (1999). The alternating decision tree learning algorithm. ICML.
  • Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalisation of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. https://doi.org/10.1006/jcss.1997.1504
  • Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29, 1189–1232.
  • Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularisation paths for generalised linear models via coordinate descent. Journal of Statistical Software, 33(1), 1. https://doi.org/10.18637/jss.v033.i01
  • Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomised trees. Machine Learning, 63(1), 3–42. https://doi.org/10.1007/s10994-006-6226-1
  • Gijare, S., Karthick, K., Juttu, S., Thipse, S. S., Paulraj, L. S., Jaiganesh, B., & Sridharan, S. (2024). Duty cycle based fuel consumption calculation using simulation methodology for agricultural tractor (0148-7191).
  • Grisso, R. D., Vaughan, D. H., & Roberson, G. T. (2006). Method for fuel prediction for specific tractor models [Paper presentation]. 2006 ASAE Annual Meeting.
  • Hardy, M. A. (1993). Regression with dummy variables (Vol. 93). Sage.
  • Harris, H., & Pearce, F. (1990). A universal mathematical model of diesel engine performance. Journal of Agricultural Engineering Research, 47, 165–176. https://doi.org/10.1016/0021-8634(90)80038-V
  • Jalilnezhad, H., Abbaspour-Gilandeh, Y., Rasooli-Sharabiani, V., Mardani, A., Hernández-Hernández, J. L., Montero-Valverde, J. A., & Hernández-Hernández, M. (2023). Use of a convolutional neural network for predicting fuel consumption of an agricultural tractor. Resources, 12(4), 46. https://doi.org/10.3390/resources12040046
  • Karparvarfard, S., & Rahmanian-Koushkaki, H. (2015). Development of a fuel consumption equation: Test case for a tractor chisel-ploughing in a clay loam soil. Biosystems Engineering, 130, 23–33. https://doi.org/10.1016/j.biosystemseng.2014.11.015
  • Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30.
  • Kheiralla, A. F., Yahya, A., Zohadie, M., & Ishak, W. (2004). Modelling of power and energy requirements for tillage implements operating in Serdang sandy clay loam, Malaysia. Soil and Tillage Research, 78(1), 21–34. https://doi.org/10.1016/j.still.2003.12.011
  • Kim, S., Kim, K., & Kim, D. (2011). Prediction of fuel consumption of agricultural tractors. Applied Engineering in Agriculture, 27(5), 705–709.
  • Kim, S.-C., Kim, K.-U., & Kim, D.-C. (2010). Modeling of fuel consumption rate for agricultural tractors. Journal of Biosystems Engineering, 35(1), 1–9. https://doi.org/10.5307/JBE.2010.35.1.001
  • Kolator, B., & Białobrzewski, I. (2011). A simulation model of 2WD tractor performance. Computers and Electronics in Agriculture, 76(2), 231–239. https://doi.org/10.1016/j.compag.2011.02.002
  • Kramer, O., & Kramer, O. (2013). K-nearest neighbors. Dimensionality reduction with unsupervised nearest neighbors, 13–23.
  • Kumar, N., & Pandey, K. (2015). A visual basic program for predicting optimum gear and throttle position for best fuel economy for 32 kW tractor. Computers and Electronics in Agriculture, 119, 217–227. https://doi.org/10.1016/j.compag.2015.10.024
  • Kumar, A. A., Tewari, V., Gupta, C., & Kumar, N. (2017). A visual basic program and instrumentation system for power and energy mapping of tractor implement. Engineering in Agriculture, Environment and Food, 10(2), 121–132. https://doi.org/10.1016/j.eaef.2016.12.003
  • MacKay, D. J. (1992). Bayesian interpolation. Neural Computation, 4(3), 415–447. https://doi.org/10.1162/neco.1992.4.3.415
  • Moghadasi, M., Ozgoli, H. A., & Farhani, F. (2021). Steam consumption prediction of a gas sweetening process with methyldiethanolamine solvent using machine learning approaches. International Journal of Energy Research, 45(1), 879–893. https://doi.org/10.1002/er.5979
  • Moitzi, G., Wagentristl, H., Refenner, K., Weingartmann, H., Piringer, G., Boxberger, J., & Gronauer, A. (2014). Effects of working depth and wheel slip on fuel consumption of selected tillage implements. Agricultural Engineering International: CIGR Journal, 16(1), 182–190.
  • Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.
  • Nagar, H., & Machavaram, R. (2022). Application of artificial intelligence for fuel consumption prediction of a tractor in different operating conditions [Paper presentation]. 2022 IEEE 7th International Conference for Convergence in Technology (I2CT). https://doi.org/10.1109/I2CT54291.2022.9824626
  • Pranav, P., & Pandey, K. (2008). Computer simulation of ballast management for agricultural tractors. Journal of Terramechanics, 45(6), 185–192. https://doi.org/10.1016/j.jterra.2008.12.002
  • Rahimi-Ajdadi, F., & Abbaspour-Gilandeh, Y. (2011). Artificial neural network and stepwise multiple range regression methods for prediction of tractor fuel consumption. Measurement, 44(10), 2104–2111. https://doi.org/10.1016/j.measurement.2011.08.006
  • Rifkin, R. M., & Lippert, R. A. (2007). Notes on regularised least squares.
  • Rubinstein, R., Zibulevsky, M., & Elad, M. (2008). Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit.
  • Shafaei, S., Loghavi, M., & Kamgar, S. (2018a). An extensive validation of computer simulation frameworks for neural prognostication of tractor tractive efficiency. Computers and Electronics in Agriculture, 155, 283–297. https://doi.org/10.1016/j.compag.2018.10.027
  • Shafaei, S., Loghavi, M., & Kamgar, S. (2018b). On the neurocomputing based intelligent simulation of tractor fuel efficiency parameters. Information Processing in Agriculture, 5(2), 205–223. https://doi.org/10.1016/j.inpa.2018.02.003
  • Shafaei, S., Loghavi, M., & Kamgar, S. (2019). Reliable execution of a robust soft computing workplace found on multiple neuro-fuzzy inference systems coupled with multiple nonlinear equations for exhaustive perception of tractor-implement performance in plowing process. Artificial Intelligence in Agriculture, 2, 38–84. https://doi.org/10.1016/j.aiia.2019.06.003
  • Siemens, J. C., & Bowers, W. (1999). Machinery management: How to select machinery to fit the real needs of farm managers. John Deere Pub.
  • Singh, S., & Singh, S. (2021). Farm power availability and its perspective in Indian agriculture. RASSA Journal of Science for Society, 3(2), 114–126.
  • Sperandio, G., Ortenzi, L., Spinelli, R., Magagnotti, N., Figorilli, S., Acampora, A., & Costa, C. (2023). A multi-step modelling approach to evaluate the fuel consumption, emissions, and costs in forest operations. European Journal of Forest Research, 143, 233–247. https://doi.org/10.1007/s10342-023-01624-2
  • Verducci, J. S., Shen, X., & Lafferty, J. (2007). Prediction and discovery: AMS-IMS-SIAM joint summer research conference, machine and statistical learning: Prediction and discovery, June 25–29, 2006, Snowbird, Utah (Vol. 443). American Mathematical Soc.