Prediction of tractor power take-off performance using artificial neural network

Karwasra, Nitin; Kumar, Anil; Kalra, Amarjit; Mukesh, S.; Rani, Vijaya

doi:10.5958/2349-4433.2022.00046.0

Cited by 2 publications

(1 citation statement)

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“…All of these variables, which are manageable, cover a wide variation of options on which choices have to be based, such as the appropriate size of the tillage implement for the tractor [79]. Moreover, the prediction of tractor drawbar performance can lead to simulation and optimization of tractor performance, allowing for the optimum setting of different parameters, as well as guiding a manufacturer in decision making for design of new tractors [80]. The STI makes average contribution percentages of 46.36%, 35.97%, and 23.42% indicated in Figure 12, during prediction of draft force, fuel consumption, and effec field capacity, respectively, using the ANN model.…”

Section: Contribution Percentage Analysismentioning

confidence: 99%

Evaluation of Artificial Neural Network to Model Performance Attributes of a Mechanization Unit (Tractor-Chisel Plow) under Different Working Variables

2022

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The focal objective of the current research is to apply artificial neural network (ANN) and multiple linear regression (MLR) methods for modeling the performance attributes of a mechanization unit (tractor-chisel plow) during the plowing process under both different soil types and working variables. Two different parameters to represent working conditions and soil type were considered as potential input parameters. The first parameter represented soil type by calculating soil texture index as a combination of clay, silt, and sand. The second one was constructed into one dimensionless parameter, namely the field working index. This index linked most working variables such as plowing speed, plow width, soil moisture content, soil bulk density, tractor power, and plowing depth. The performance of the created ANN and MLR models was appraised by computing mean-absolute-error criterion for the testing dataset. The mean absolute error values for draft force, effective field capacity, fuel consumption, drawbar power, overall energy efficiency, rate of plowed soil volume, and loading factor, were 1.44 kN, 0.03 ha/h, 1.17 L/h, 2.28 kW, 0.68%, 73.97 m3/h, and 0.08 (decimal), respectively, when the ANN model was applied. In addition, coefficient of determination (R2) acted as a criterion for judging the performance of the developed models, and their values when ANN was applied were 0.569, 0.384, 0.516, 0.454, 0.486, 0.777, and 0.730 for the same performance attributes, respectively. When the MLR model was applied, the corresponding values of R2 were 0.239, 0.358, 0.352, 0.429, 0.511, 0.482, and 0.422, respectively, for the same attributes. The current study adds to the standing literature by contributing data and information regarding the performance attributes of a tractor-chisel plow unit under specific working variables and soil types. In addition, the models developed for plowing operations in different soil texture and under the field working index are recommended for use in budgeting for diesel consumption, in calculating operation cost, in matching mechanization units of tractor-chisel plows, in estimating energy requirements of tractor-chisel plow combinations, etc.

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Section: Contribution Percentage Analysismentioning

confidence: 99%

Evaluation of Artificial Neural Network to Model Performance Attributes of a Mechanization Unit (Tractor-Chisel Plow) under Different Working Variables

2022

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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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Prediction of tractor power take-off performance using artificial neural network

Cited by 2 publications

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Evaluation of Artificial Neural Network to Model Performance Attributes of a Mechanization Unit (Tractor-Chisel Plow) under Different Working Variables

Evaluation of Artificial Neural Network to Model Performance Attributes of a Mechanization Unit (Tractor-Chisel Plow) under Different Working Variables

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

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