Prediction of Combine Harvester Performance Using Hybrid Machine Learning Modeling and Response Surface Methodology

  • Tarahom Mesri Gundoshmian
  • Sina Ardabili
  • Amir MosaviEmail author
  • Annamária R. Várkonyi-Kóczy
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 101)


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.


Combine harvester Hybrid machine learning ANFIS Response surface methodology (RSM) Artificial intelligence in agriculture Radial basis function (RBF) 



This publication has been supported by the Project: “Support of research and development activities of the J. Selye University in the field of Digital Slovakia and creative industry” of the Research & Innovation Operational Programme (ITMS code: NFP313010T504) co-funded by the European Regional Development Fund.


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Authors and Affiliations

  1. 1.Department of Biosystem EngineeringUniversity of Mohaghegh ArdabiliArdabilIran
  2. 2.Institute of Advanced Studies KoszegKoszegHungary
  3. 3.Kalman Kando Faculty of Electrical EngineeringObuda UniversityBudapestHungary
  4. 4.School of the Built EnvironmentOxford Brookes UniversityOxfordUK
  5. 5.Department of Mathematics and InformaticsJ. Selye UniversityKomarnoSlovakia

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