Modification of Values for the Horizontal Force of Tillage Implements Estimated from the ASABE Form Using an Artificial Neural Network

The famous empirical model for the horizontal force estimation of farm implements was issued by American Society Agricultural Biological Engineers (ASABE). It relies on information soil texture through its adjustment parameter, which is called Fi -parameter. Fi-parameter not measurable, and geometry plow machine parameter values are measurable; however, tillage speed, implement width, depth measurable. In this study, calibrated using a regression technique based norm that combines sand, silt, clay contents with R2 0.703. A feed-forward artificial neural network (ANN) backpropagation algorithm training purposes established to estimate modified four inputs: working field criterion, norm, initial moisture content, (which estimated ASABE standard new—Fi-parameter). Our developed ANN had high coefficient determination (R2) their in training, testing, validation stages were 0.8286, 0.8175, 0.8515, respectively demonstrated applicability prediction forces. An Excel spreadsheet created weights specific implements, such as disk, chisel, or moldboard plows. tested data plow; addition, good required percentage error 10% achieved. contributed toward numerical method can be used agricultural engineers future. Furthermore, we also concluded equations presented study formulated any computer language create simulation program predict requirements implement.