Article Sidebar

Download
PDF
Statistic
Read Counter : 399 Download : 177

Downloads

Download data is not yet available.

Main Article Content

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.

Keywords

ANN Design-Expert software Bulk density Cone index

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite
Salim, A.-E. A. ., Almaliki, S. A. ., & Nedawi, D. R. . (2022). Smart Computing Techniques for Predicting Soil Compaction Criteria under Realistic Field Conditions. Basrah Journal of Agricultural Sciences, 35(1), 188–211. https://doi.org/10.37077/25200860.2022.35.1.15
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Almaliki, S. A., Himoud, M. S., & Muhsin, S. J. (2021). Mathematical model for evaluating slippage of tractor under various field conditions. Basrah Journal of Agricultural Sciences, 34, 49-59. https://doi.org/10.37077/25200860.2021.34.1.05
  2. Almaliki, S., Alimardani, R., & Omid, M. (2016). Artificial neural network based modeling of tractor performance at different field conditions. Agricultural Engineering International: CIGR Journal, 18, 262-274. https://cigrjournal.org/index.php/Ejounral/article/view/3880
  3. Almaliki, S., Himoud, M., & Al-Khafajie, A. (2019). Artificial neural network and stepwise approach for predicting tractive efficiency of the tractor (CASE JX75T). The Iraqi Journal of Agricultural Science, 50, 1008-1017 https://doi.org/10.36103/ijas.v50i4.745
  4. Antille, D. L., Ansorge, D., Dresser, M. L., & Godwin, R. J. (2013). Soil displacement and soil bulk density changes as affected by tire size. Transactions of the American Society of Agricultural and Biological Engineers (ASABE), 1683-1693. https://doi.org/doi:10.13031/trans.56.9886
  5. Arvidsson, J., & Keller, T. (2004). Soil precompression stress. I. A survey of Swedish arable soils. Soil and Tillage Research, 77, 85-95. http://doi.org/10.1016/j.still.2004.01.003
  6. Arvidsson, J., Sjöberg, E., & van den Akker, J. J. H. (2003). Subsoil compaction caused by heavy sugarbeet harvesters in southern Sweden; III. Risk assessment using a soil water model. Soil & Tillage Research, 73, 77-87. https://doi.org/10.1016/S0167-1987(03)00101-6
  7. Arvidsson, J., Westlin, H., Keller, T., & Gilbertsson, M. (2011). Rubber track systems for conventional tractors – Effects on soil compaction and traction. Soil and Tillage Research, 117, 103-109. http://doi.org/10.1016/j.still.2011.09.004
  8. ASAE, Standard. (2009). ASAE D497.6 Agricultural Machinery Management Data. ASAE. St. Joseph. MI: 49085, 1-8. https://cutt.ly/EfMlj1q
  9. Batey, T. (2009). Soil compaction and soil management: A review. Soil Use and Management, 25, 335-345. http://doi.org/10.1111/j.1475-2743.2009.00236.x
  10. Black, C. A. (1965). Methods of soil analysis Part 1, Part 1. Madison, Wisconsin, American Society of Agronomy. http://www.worldcat.org/oclc/85962062
  11. Błaszkiewicz, Z. (2019). Identification and quantification of selected factors determining soil compression by tractors of weights with single wheels and dual wheels. Journal of Research and Applications in Agricultural Engineering, 64, 4-12. http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-e623094d-a477-470a-9941-26b166cf3020
  12. Canarache, A., Horn, R., & Colibas, I. (2000). Compressibility of soils in a long term field experiment with intensive deep ripping in Romania. Soil and Tillage Research, 56, 185-196. http://doi.org/10.1016/S0167-1987(00)00143-4
  13. D’Acqui, L. P., Certini, G., Cambi, M., & Marchi, E. (2020). Machinery’s impact on forest soil porosity. Journal of Terramechanics, 91, 65-71. https://doi.org/10.1016/j.jterra.2020.05.002
  14. Demuth, H. and Beale, M. (1998) Neural Network Toolbox for Use with MATLAB, User’s Guide, Version 3. The MathWorks Inc., Natick.
  15. Défossez, P., Richard, G., Boizard, H., & O'Sullivan, M.F. (2003). Modeling change in soil compaction due to agricultural traffic as function of soil water content. Geoderma, 116, 89-105. https://doi.org/10.1016/S0016-7061(03)00096-X
  16. Filipovic, D., Husnjak, S., Kosutic, S., & Gospodaric, Z. (2006). Effects of tillage systems on compaction and crop yield of Albic Luvisol in Croatia. Journal of Terramechanics, 43, 177-189. http://doi.org/10.1016/j.jterra.2005.04.002
  17. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). A review of the use of convolutional neural networks in agriculture. The Journal of Agricultural Science, 156, 312-322. https://doi.org/10.1017/s0021859618000436
  18. Keller, T., & Arvidsson, J. (2016). A model for prediction of vertical stress distribution near the soil surface below rubber-tracked undercarriage systems fitted on agricultural vehicles. Soil and Tillage Research, 155, 116-123. http://doi.org/10.1016/j.still.2015.07.014
  19. Krueger, T., Quinton, J. N., Freer, J., Macleod, C., Bilotta, G. S., Brazier, R. E., & Hawkins, P. (2012). Comparing empirical models for sediment and phosphorus transfer from soils to water at field. European Journal of Soil Science, 63, 211-223. https://doi.org/10.1111/j.1365-2389.2011.01419.x
  20. Liu, Q., & Shalaby, A. (2013). Simulation of pavement response to tire pressure and shape of contact area. Canadian Journal of Civil Engineering, 40, 236-242. https://doi.org/10.1139/cjce-2011-0567
  21. Marra, E., Cambi, M., Fernandez-Lacruz, R., Giannetti, F., Marchi, E., & Nordfjell, T. (2018). Photogrammetric estimation of wheel rut dimensions and soil compaction after increasing numbers of forwarder passes. Scandinavian Journal of Forest Research, 33, 613-620. https://doi.org/10.1080/02827581.2018.1427789
  22. Monjezi, N. (2021). Energy prediction of wheat production using data mining technique in Iran. Basrah Journal of Agricultural Sciences, 34, 14-27. https://doi.org/10.37077/25200860.2021.34.1.02
  23. Monjezi, N., & Hosseinzadeh, E. (2021). An assessment of energy consumption for canola production system in Iran a case study: A mirkabir agro-industry). Basrah Journal of Agricultural Sciences, 34, 28-37. https://doi.org/10.37077/25200860.2021.34.1.03
  24. Montgomery, D. C., & Runger, G. C., (2014). Applied statistics and probability for engineers. John Wiley & Sons, Inc., Hoboken, NJ, xvi, 765pp.
  25. Naranjo, S., Sandu, C., Taheri, S., & Taheri, S. (2014). Experimental testing of an off-road instrumented tire on soft soil. Journal of Terramechanics, 56, 119-137. https://doi.org/10.1016/j.jterra.2014.09.003
  26. Pagliai, M., Marsili, A., Servadio, P., Vignozzi, N., & Pellegrini, S. (2003). Changes in some physical properties of a clay soil in central Italy following the passage of rubber tracked and wheeled tractors of medium power. Soil and Tillage Research, 73, 119-129. https://doi.org/10.1016/s0167-1987(03)00105-3
  27. Peng, X. H., & Horn, R. (2008). Time-dependent, anisotropic pore structure and soil strength in a 10-year period after intensive tractor wheeling under conservation and conventional tillage. Journal of Plant Nutrition and Soil Science, 171, 936-944. https://doi.org/10.1002/jpln.200700084
  28. RNAM, (1995). Rnam Test Codes and Procedures for Farm Machinery. Bangkok: Print. http://www.worldcat.org/oclc/224884306
  29. Rücknagel, J., Hofmann, B., Paul, R., Christen, O., & Hülsbergen, K. J. (2007). Estimating precompression stress of structured soils on the basis of aggregate density and dry bulk density. Soil and Tillage Research, 92, 213-220. https://doi.org/10.1016/j.still.2006.03.004
  30. Santos, F. L., Jesus, V. A. M. de, & Valente, D. S. M. (2012). Modeling of soil penetration resistance using statistical analyses and artificial neural networks. Acta Scientiarum Agronomy, 34, 219-224. https://doi.org/10.4025/actasciagron.v34i2.11627
  31. Shafaei, S., Loghavi, M., & Kamgar, S. (2018). On the neurocomputing based intelligent simulation of tractor fuel efficiency parameters. Information Processing in Agriculture, 5, 205-223. https://doi.org/10.1016/j.inpa.2018.02.003
  32. Shahgholi, G., & Abuali, M. (2015). Measuring soil compaction and soil behavior under the tractor tire using strain transducer. Journal of Terramechanics, 59, 19-25. https://doi.org/10.1016/j.jterra.2015.02.007
  33. Sivarajan, S., Maharlooei, M., Bajwa, S. G., & Nowatzki, J. (2018). Impact of soil compaction due to wheel traffic on corn and soybean growth, development and yield. Soil & Tillage Research, 175, 234-243. https://doi.org/10.1016/j.still.2017.09.001
  34. Taghavifar, H., & Mardani, A. (2014). Applying a supervised ANN (artificial neural network) approach to the prognostication of driven wheel energy efficiency indices. Energy, 68, 651-657. https://doi.org/10.1016/j.energy.2014.01.048
  35. Tang, C.-S., Wang, D.-Y., Shi, B., & Li, J. (2016). Effect of wetting–drying cycles on profile mechanical behavior of soils with different initial conditions. CATENA, 139, 105-116. http://doi.org/10.1016/j.catena.2015.12.015
  36. Zhukov, A. V. (2015). Influence of usual and dual wheels on soil penetration resistance: The GIS-approach. Ukrainian Journal of Ecology, 5, 73. https://doi.org/10.7905/bbmspu.v5i3.1114