  Novel applications of artificial intelligence for tuning the parameters of
industrial machines for optimal performance are emerging at a fast pace. Tuning
the combine harvesters and improving the machine performance can dramatically
minimize the wastes during harvesting, and it is also beneficial to machine
maintenance. Literature includes several soft computing, machine learning and
optimization methods that had been used to model the function of harvesters of
various crops. Due to the complexity of the problem, machine learning methods
had been recently proposed to predict the optimal performance with promising
results. In this paper, through proposing a novel hybrid machine learning model
based on artificial neural networks integrated with particle swarm optimization
(ANN-PSO), the performance analysis of a common combine harvester is presented.
The hybridization of machine learning methods with soft computing techniques
has recently shown promising results to improve the performance of the combine
harvesters. This research aims at improving the results further by providing
more stable models with higher accuracy.
