Performance Evaluation of Artificial Neural Network Modelling to a Ploughing Unit in Various Soil Conditions

Abstract

The specific objective of this study is to find a suitable artificial neural network model for estimating the operation indicators (disturbed soil volume, effective field capacity, draft force, and energy requirement) of ploughing units (tractor disc) in various soil conditions. The experiment involved two different factors, i.e., (Ι) soil texture index and (ΙΙ) field work index, and included soil moisture content, tractor engine power, soil bulk density, tillage speed, tillage depth, and tillage width, which were linked to one dimensionless index. We assessed the effectiveness of artificial neural network and multiple linear regression models between the values predicted and the actual values using the mean absolute error criterion to test data points. When the artificial neural network model was applied, the mean absolute error values for disturbed soil volume, effective field capacity, draft force, and energy requirement were 69.41 m3·hr−1, 0.04 ha·hr−1, 1.24 kN, and 1.95 kw·hr·ha−1, respectively. In order to evaluate the behaviour of new models, the coefficient R 2 was used as a criterion, where R 2 values in artificial neural network were 0.9872, 0.9553, 0.9948, and 0.9718, respectively, for the aforementioned testing dataset. Simultaneously, R 2 values in multiple linear regression were 0.7623, 0.696, 0.492, and 0.5572, respectively, for the same testing dataset. Based on these comparisons, it was clear that predictions using the artificial neural network models proposed are very satisfactory.

Prediction of Specific Fuel Consumption of a Tractor during the Tillage Process Using an Artificial Neural Network Method

“…The soil texture in this study is presented as the soil texture index as described in Dahham et al [22], as follows:…”

“…We obtained 497 data points for data collected from the literature for chisel and moldboard plows, which were randomly separated into training and testing datasets by our chosen software in a ratio of 80:20. In the study of Dahham et al [22], the ANN model to predict the draft force of a disk plow was formed based on 375 samples reserved from field experiments. Using Equation (6), the data of input and output parameters were normalized into a range of 0.15 to 0.85:…”

Identification of Armyworm-Infected Leaves in Corn by Image Processing and Deep Learning

“…This means all images in the training set and all test images need to be of size 256 × 256. If the input image is not 256 × 256, it needs to be converted to 256 × 256 before using it for training the network (Pourdarbani et al, 2023;Dahham et al, 2023). AlexNet is a complex model with many parameters, making it computationally expensive and time-consuming to train.…”

Optimisation and Modelling of Soil Pulverisation Index Using Response Surface Methodology for Disk Harrow Under Different Operational Conditions

“…Computer programs for modelling have been broadly developed. Consequently, researchers have utilised these models to predict the performance of soil bed preparation equipment based on operation circumstances and type of tillage machines (Cviklovič et al, 2021;Abrahám et al, 2022;Dahham et al, 2023). The regression model created by Abbaspour-Gilandeh and Sedghi (2015) based on a fuzzy modeling method produced an R 2 of 0.787%, e of 17.6%, and RMSE of 0.706% for soil pulverisation for disk harrows.…”