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. Col… Show more
“…A flowchart labeling the different solving stages for developing the ANN model using Qnet2000 is explained in Fig. 2 29 . Figure 2 Qnet 2000 software procedures for developing an ANN model 29 .…”
Section: Methodsmentioning
confidence: 99%
“…A flowchart labeling the different solving stages for developing the ANN model using Qnet2000 is explained in Fig. 2 29 .…”
Section: Multiple Linear Regression Model (Mlr)mentioning
As artificial neural networks (ANNs) have been shown to be precise and reliable in supporting the field of artificial intelligence technology, agricultural scientists have focused on employing ANN for agricultural applications. The ANN can be an effective alternative for evaluating agricultural operations. The intended aim of this investigation was to employ both ANN and multiple linear regression (MLR) to develop a model for determining the rear wheel slip of an agricultural tractor in two-wheel drive (2WD) mode during plowing operations. The output parameter of the models was tractor rear wheel slip. The training data were collected from filed experiments using chisel, moldboard, and disk plows. The plows were operated under different conditions of soil texture, plowing depth, soil moisture content, and plowing speed. All data were acquired during field experiments in two soil textures (clay and clay loam textures). The training dataset was comprised of 319 data points, while 65 data points were employed to test both ANN and MLR models estimation capability. The ANN model with a backpropagation training algorithm was created using the commercial Qnet2000 software by changing its topology and related parameters. The best ANN model possessed a topology of 7-20-1. The estimated tractor rear wheel slip using the testing dataset displayed strong agreement with measured tractor rear wheel slip with the coefficient of determination (R2) value of 0.9977. The results definitely illustrated that the ANN model was capable of defining the correlation between the inputs and rear wheel slip. The outcomes suggest that the established ANN model is trustworthy in predicting the tractor rear wheel slip for an agricultural tractor in 2WD mode during the tillage process compared to MLR models. This study provides a useful tool for management of tillage implements during field operations.
“…A flowchart labeling the different solving stages for developing the ANN model using Qnet2000 is explained in Fig. 2 29 . Figure 2 Qnet 2000 software procedures for developing an ANN model 29 .…”
Section: Methodsmentioning
confidence: 99%
“…A flowchart labeling the different solving stages for developing the ANN model using Qnet2000 is explained in Fig. 2 29 .…”
Section: Multiple Linear Regression Model (Mlr)mentioning
As artificial neural networks (ANNs) have been shown to be precise and reliable in supporting the field of artificial intelligence technology, agricultural scientists have focused on employing ANN for agricultural applications. The ANN can be an effective alternative for evaluating agricultural operations. The intended aim of this investigation was to employ both ANN and multiple linear regression (MLR) to develop a model for determining the rear wheel slip of an agricultural tractor in two-wheel drive (2WD) mode during plowing operations. The output parameter of the models was tractor rear wheel slip. The training data were collected from filed experiments using chisel, moldboard, and disk plows. The plows were operated under different conditions of soil texture, plowing depth, soil moisture content, and plowing speed. All data were acquired during field experiments in two soil textures (clay and clay loam textures). The training dataset was comprised of 319 data points, while 65 data points were employed to test both ANN and MLR models estimation capability. The ANN model with a backpropagation training algorithm was created using the commercial Qnet2000 software by changing its topology and related parameters. The best ANN model possessed a topology of 7-20-1. The estimated tractor rear wheel slip using the testing dataset displayed strong agreement with measured tractor rear wheel slip with the coefficient of determination (R2) value of 0.9977. The results definitely illustrated that the ANN model was capable of defining the correlation between the inputs and rear wheel slip. The outcomes suggest that the established ANN model is trustworthy in predicting the tractor rear wheel slip for an agricultural tractor in 2WD mode during the tillage process compared to MLR models. This study provides a useful tool for management of tillage implements during field operations.
“…We noticed that the total corrected percentage (87.7%), as shown in Figure 2 , of the testing dataset stops early if the performance starts to degrade on a validation dataset. However, a flow chart tagging the different solving steps for establishing the present ANN model using the applied software of Qnet2000 is explained in a previous study [ 40 ].…”
The grape is a very well-liked fruit that is valued for its distinct flavor and several health benefits, including antioxidants, anthocyanins, soluble sugars, minerals, phenolics, flavonoids, organic acids, and vitamins, which significantly improve the product’s overall quality. Today’s supply chain as a whole needs quick and easy methods for evaluating fruit quality. Thus, the objective of this study was to estimate the quality attributes of Flame Seedless grape berries cultivated under various agronomical management and other practices using color space coordinates (berry L*, berry a*, and berry b*) as inputs in an artificial neural network (ANN) model with the best topology of (3-20-11). Satisfactory predictions based on the R2 range, which was 0.9817 to 0.9983, were obtained for physical properties (i.e., berry weight, berry length, and berry diameter as well as berry adherence strength) and chemical properties (i.e., anthocyanin, total soluble solids (TSS), TSS/titratable acidity, total sugars, titratable acidity, reducing sugars, and non-reducing sugars). Meanwhile, we also performed a contribution analysis to analyze the relative importance of CIELab colorimeter parameters of berries L*, a*, and b* to determine the main fruit quality. In terms of relative contribution, berry b* contributed relatively largely to berry weight, berry adherence strength, TSS, TSS/titratable acidity, titratable acidity, total sugars, reducing sugars, and non-reducing sugars and a* contributed relatively largely to anthocyanin, berry length, and berry diameter. The developed ANN prediction model can aid growers in enhancing the quality of Flame Seedless grape berries by selecting suitable agronomical management and other practices to avoid potential quality issues that could affect consumers of them. This research demonstrated how color space coordinates and ANN model may well be utilized to evaluate the Flame seedless grape berries’ quality.
“…The output layer included seven attributes, namely the draft force, drawbar power, effective field capacity, overall energy efficiency, fuel consumption, rate of plowed soil volume, and loading factor. A flowchart labeling the different stages used to build the ANN model with the commercially available Qnet2000 package (the publisher is Vesta Services Inc., Winnetka, IL, USA, version 1.0, https://qnetv2kt.software.informer.com/1.0/, accessed on 27 April 2022) [58] is presented in a former study by Marey et al [59]. Howevere, Qnet2000 is a multi-layer perceptron whose training is achieved using a back-propagation algorithm.…”
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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