PREDICTING THE PERFORMANCE PARAMETERS OF CHISEL PLOW USING NEURAL NETWORK MODEL

ABSTRACT This study examines the capability of an artificial neural network (ANN) approach using a backpropagation-learning algorithm to predict performance parameters for a chisel plow at three field sites with differing soils. The draft force, effective field capacity (EFC), fuel consumption rate (FC), overall energy efficiency (OEE), and rate of plowed soil volume (SVR) were predicted at varying plowing speeds, plowing depths, soil moisture contents, soil bulk densities, soil texture indexes, and tractor powers. Collected field data was divided into a training set (for predicting the required parameters) and testing set (for model validation). For the ANN algorithm, the number of hidden layers, neurons, and transfer functions were varied to construct different ANN architectures, which were then verified using various statistical criteria, such as mean absolute error. The results showed that an ANN with one hidden layer and 15 neurons was ideal. The developed ANN model predicted the draft force, EFC, FC, OEE, and SVR of the chisel plow with a mean absolute error of 3.23 kN, 0.80 hah-1, 3.04 Lh-1, 2.78% and 1.06 m3h−1, respectively in the testing phase.