An artificial neural network (ANN) model, with a back propagation learning algorithm, was developed to predict draft\nrequirements of two winged share tillage tools in a loam soil. The input parameters to the 3ââ?¬â??7ââ?¬â??1 ANN model were; share width,\nworking depth and operating speed. The output from the network was the draft requirement of each tillage tool. The developed\nmodel predicted the draft requirements of the winged share tillage tools with a mean relative error of 0.56 and mean square errors\nof 0.049, when compared to measured draft values. This result indicates that the ANN model had successfully learnt from the\ntraining data set to enable correct interpolation and could be used as an alternative tool for modeling soil-tool interaction under\nspecific experimental conditions and soil types.
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