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

Tarahom Mesri Gundoshmian; Sina Ardabili; Amir Mosavi; Annamária R. Várkonyi-Kóczy

doi:10.20944/preprints201908.0202.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 16 August 2019 / Approved: 20 August 2019 / Online: 20 August 2019 (08:03:38 CEST)

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)

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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

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