Prediction of Combine Harvester Performance Using Hybrid Machine Learning Modeling and Response Surface Methodology<strong> </strong>

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.

COVID-19 Outbreak Prediction with Machine Learning

“…Using ML techniques, the computer learns to use patterns or “training samples” in data (processed information) to predict or make intelligent decisions without overt planning [71,72]. In other words, ML is the scientific study of algorithms and statistical models used by computer systems that use patterns and inference to perform tasks instead of using explicit instructions [73,74].…”