Predicting and Optimizing Tillage Draft using Artificial Network Technique

Shehta, Yasmin; Kabany, Abdel-Fadil; Elhelew, Waleed; Abdelwahed, Mohammed

doi:10.21608/ajs.2023.171425.1500

Cited by 1 publication

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“…Additionally, the model's accuracy may decrease when applied to data with significant differences from the training dataset [80]. Various studies are available on the draft force, which is modeled by ANN method, for tillage implements [81]; however, the former ANN modeling approaches did not attempt to modify the draft force of ASABE draft form [12]. Hence, the authors of this paper presented this applied ANN model for predicting the modified draft force of the famous primary tillage implements that are used for farm machinery management purposes.…”

Section: Performance Of the Development Of The Ann Modelmentioning

confidence: 99%

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

et al. 2023

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

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Section: Performance Of the Development Of The Ann Modelmentioning

confidence: 99%