Smart Computing Techniques for Predicting Soil Compaction Criteria under Realistic Field Conditions

Abstract

The primary objective of this paper was to develop an artificial neural network (ANN) simulation environment and mathematical models for predicting with high accuracy soil compression parameters. The experiments were conducted at the College of Agriculture - University of Basra, located at Garmat Ali, the soil was silty clay loam. The factors that were investigated are moisture content (14 and 24%), tillage depths (0, 15, 30, 45, and 50 cm) forward speeds (0.57, 0.94, and 1.34 m.s-1) and tire pressures (50, 100, and 150 kPa). ANN environment was developed with the back propagation algorithm using MATLAB software with various structures and training algorithms. Design Expert software utilized to evaluate the studied parameters and produce mathematical models. The results showed that all studied parameters had a significant effect on soil physical properties including bulk density and cone index. The effects of the studied factors on bulk density were depth > moisture content > forward speed, > tire pressure (6% 4%, 2.4%, 2%, respectively). Whereas, the order of the investigated factors based on their effects on cone index were depth > moisture content > tire pressure > forward speed (6%, 4%, 2.4% and 2%, respectively). The best model for predicting the bulk density under different field conditions was the 4-8-1 architecture. Levenberg-Marquardt (Trainlm) produced outstanding performance with an MSE of 0.00226 and R2 of 0.986. Moreover, this performance was occurring at an epoch of 100. For predicting cone index, the best performance was achieved by Levenberg-Marquardt (trainlm) in 85 epochs, giving minimum MSE equal to 0.005112 and greater (R2) equal to 0.967 during the training process. Thus, the optimal structure for predicting cone index was 4-7-1.

Prediction of Soil Compaction using Conventional Tillage Systems under Different Operating Conditions

“…are consistent with previous studies by [21,2,23,29,36]. Moreover, the increase in the total porosity of the soil was observed for both the reversible disc plow and the chisel plow, respectively.…”

“…The use of the disc plow in plowing led to an increase in soil compaction and a decrease in the yield of tobacco leaves through an increase in the indicators of bulk density and penetration resistance [18]. [19] confirmed, through the data obtained from an experiment to know the effect of plows on soil compaction, that plowing using the moldboard plow and disc plow and plowing annually caused soil compaction at the depth of plowing during 3 years, as the soil penetration resistance reached 3.75 MPa and at a depth of (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) cm. In an experiment conducted by [20] to know the effect of the plowing system using the disc plow on the bulk density and soil resistance to penetration at different depths, an increase in the studied properties was found under the studied plowing systems with increasing depth, and it led to the formation of a compacted layer with a high bulk density at a plowing depth of 20-30 cm, amounting to 1.60 grams.…”

“…The so-called plow basin was found, and the amount of soil penetration resistance reached about 2000 kPa, and the reason for this is due to tilling at one depth and for many periods. [12] confirmed in a study conducted to find out the effect of traditional and reduced plowing on soil properties, where he found a compacted layer at a depth of (28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38) cm called the plow basin, and showed through the use of electronic rays that this layer extends to about 55 cm. The reason for this is plowing at one depth for years and with various agricultural machines, as the soil is subjected to continuous and vertical stresses.…”

“…The soil was of silty clay texture. The initial samples were taken for the physical and mechanical measurements of the primary soil in the field, which includes soil texture, soil moisture content, bulk density, true density, and soil resistance to penetration before conducting the experiment, by identifying five sites in the form of the letter X, which cover all areas of the field, taking samples and using Three repetitions for three depths (0-10), (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) cm, and according to the scientific methods used in taking measurements and field and laboratory analyzes. The preliminary results were obtained as attached in…”

“…(%) at depths (0-10) cm and (10-20)%, respectively. At depth (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), the soil resistance to penetration was less by 11%. At the same depth, the soil resistance to penetration increased with the increase in the forward speed of the tractor and in the same proportions.…”