Modeling of Draft and Energy Requirements of a Moldboard Plow Using Artificial Neural Networks Based on Two Novel Variables

Abstract

Draft and energy requirements are the most important factors in the activities of farm machinery management owing to their role in matching the tractor with implements for different tillage operations. This study's aim was to model the draft and energy requirements of a moldboard plow based on two novel variables. The first was the soil texture index (STI), which was formed from the clay, sand, and silt contents with a range of 0.03-0.84. The second variable was the field working index (FWI), formed by combining the plow width, plowing speed, soil bulk density, soil moisture content, plowing depth, and tractor power into one dimensionless variable, which had a range of 7.17-82.45. The coefficient of determination (R 2 ) values obtained using a testing dataset were found out to be 0.9134 for energy and 0.8602 for draft requirements. For the draft and energy requirements of the testing data points, the mean absolute errors between the measured values and the values predicted using the artificial neural networks (ANN) model were 0.99 kN and 2.39 kW•h/ha, respectively. Based on comparisons with other results reported using multiple linear regression, it was clear that the predictions by the proposed ANN model were very satisfactory.

Predicting Soil Productivity Resulted from Organic Matter Addition by Using Neural Networks

“…From Table (5) the RMSE between measured and predicted for Bd, AW, I, Pro and WUE were 0.00909 Mg/m 3 , 0.10528 %, 0.23878 mm/h, 14.28973 kg/fed and 0.26762 kg/m 3 . While the R 2 were equal to 0.99955, 0.99947, 0.99902, 0.99998 and 0.96883 respectively (Warmling et al, 2019 andAl-Janobi et al, 2020). The high correlation coefficient for outputs parameters recall indicated for excellent prediction of ANN model for data has never seen before.…”

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

“…Oskoui and Harvey [31] verified that the STI fluctuates for different arrangements of sand, silt, and clay. The second adaptable parameter was denoted by FWI (dimensionless), which is well-defined as follows [32]:…”

“…Loading factor (decimal) = 0.841 + 0.067 × STI − 0.012 × FWI R 2 = 0.460 (32) Taking into consideration the above parameters, these established simple equations are not available in literature. Hence, the present attempt therefore is important for first identifying the parameters affecting the performance attributes of a tractor-chisel unit and for decision making in selecting such arrangements.…”

Prediction of Subsoiling Draft Requirements of Silt Loam Soils From Empirical Data: Artificial Neural Network Versus Multiple Linear Regression

“…Artificial Neural Networks (ANN) is used to model non-linear variables with unknown interactions. ANNs can learn, generalize on knowledge, perform abstraction, and make errors (Al-Janobi et al, 2020). Various researchers have used ANN in tillage draft prediction (Abbaspour-Gilandeh et al, 2020).…”

“…ANNs can handle a large amount of data, separate nonlinear independent and non-independent relationships, and determine the relative importance of different input parameters. However, their disadvantages include being a black box, over fitting and under fitting problems of the network, and the experimental nature of model construction (Al-Janobi, 2020).…”