Gundoshmian, T.M.; Ardabili, S.; Mosavi, A.; Várkonyi-Kóczy, A.R. Prediction of Combine Harvester Performance Using Hybrid Machine Learning Modeling and Response Surface Methodology. Preprints2019, 2019080202. https://doi.org/10.20944/preprints201908.0202.v1
APA Style
Gundoshmian, T.M., Ardabili, S., Mosavi, A., & Várkonyi-Kóczy, A.R. (2019). Prediction of Combine Harvester Performance Using Hybrid Machine Learning Modeling and Response Surface Methodology<strong> </strong>. Preprints. https://doi.org/10.20944/preprints201908.0202.v1
Chicago/Turabian Style
Gundoshmian, T.M., Amir Mosavi and Annamária R. Várkonyi-Kóczy. 2019 "Prediction of Combine Harvester Performance Using Hybrid Machine Learning Modeling and Response Surface Methodology<strong> </strong>" Preprints. https://doi.org/10.20944/preprints201908.0202.v1
Abstract
Automated controlling the harvesting systems can significantly increase the efficiency of the agricultural practices and prevent food wastes. Modeling and improvement of the combine harvester can increase the overall performance. Machine learning methods provide the opportunity of advanced modeling for accurate prediction of the highest performance of the machine. In this study, the modeling of combine harvesting id performed using radial basis function (RBF) and the hybrid machine learning method of adaptive neuro-fuzzy inference system (ANFIS) to predict various variables of the combine harvester for the optimal performance. Response surface methodology (RSM) is also used to optimize the models. The comparative study shows that the ANFIS method outperforms the RBF method.
Keywords
combine harvester; hybrid machine learning; ANFIS; response surface methodology (RSM); artificial intelligence in agriculture; radial basis function (RBF)
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
