Modeling of Soil Compaction Beneath the Tractor Tire using Multilayer Perceptron Neural Networks

Introduction Soil compaction is one of the most destructive effects of machine traffic. Compaction increases soil mechanical strength and reduces its porosity, plant rooting and ultimately the yield. Nowadays, agricultural machinery has the maximum share on soil compaction in modern agriculture. The...

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Autores principales:	Gh Shahgholi, H Ghafouri Chiyaneh, T Mesri Gundoshmian
Formato:	article
Lenguaje:	EN FA
Publicado:	Ferdowsi University of Mashhad 2018
Materias:	artificial neural network modeling multilayer perceptron soil compaction Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/5f48000d690249c8bb7cb80d9aa621bc
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spelling	oai:doaj.org-article:5f48000d690249c8bb7cb80d9aa621bc2021-11-14T06:34:26ZModeling of Soil Compaction Beneath the Tractor Tire using Multilayer Perceptron Neural Networks2228-68292423-394310.22067/jam.v8i1.58891https://doaj.org/article/5f48000d690249c8bb7cb80d9aa621bc2018-03-01T00:00:00Zhttps://jame.um.ac.ir/article_32669_ea210c8ba64c03d24a172e832cc59975.pdfhttps://doaj.org/toc/2228-6829https://doaj.org/toc/2423-3943Introduction Soil compaction is one of the most destructive effects of machine traffic. Compaction increases soil mechanical strength and reduces its porosity, plant rooting and ultimately the yield. Nowadays, agricultural machinery has the maximum share on soil compaction in modern agriculture. The soil destruction may be as surface deformation or as subsurface compaction. Any way machine traffic destructs soil structure and as result has unfavorable effect on the yield. Hence, soil compaction recognition and its management are very important. In general, soil compaction is the most destructive effect of machine traffic. Modeling of ecological systems by conventional modeling methods due to the multitude effective parameters has always been challenging. Artificial intelligence and soft computing methods due to their simplicity, high precision in simulation of complex and nonlinear processes are highly regarded. The purpose of this research was the modeling of soil compaction system affected by soil moisture content, the tractor forward velocity and soil depth by multilayer perceptron neural network. Materials and Methods In order to carry out the field experiments, a tractor MF285 which was equipped with a three-tilt moldboard plough was used. Experiments were conducted at the Agricultural research field of University of Mohaghegh Ardabili in five levels of moisture content of 11, 14, 16, 19 and 22%, forward velocity of 1, 2, 3, 4 and 5 km/h, and soil depths of 20, 25, 30, 35 and 40 cm as a randomized complete block design with three replications. In this study, perceptron neural network with five neurons in the hidden layer with sigmoid transfer function and linear transfer function for the output neuron was designed and trained. Results and Discussion Field experiments showed three main factors were significant on the bulk density (PGh ShahgholiH Ghafouri ChiyanehT Mesri GundoshmianFerdowsi University of Mashhadarticleartificial neural networkmodelingmultilayer perceptronsoil compactionAgriculture (General)S1-972Engineering (General). Civil engineering (General)TA1-2040ENFAJournal of Agricultural Machinery, Vol 8, Iss 1, Pp 105-118 (2018)
institution	DOAJ
collection	DOAJ
language	EN FA
topic	artificial neural network modeling multilayer perceptron soil compaction Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	artificial neural network modeling multilayer perceptron soil compaction Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040 Gh Shahgholi H Ghafouri Chiyaneh T Mesri Gundoshmian Modeling of Soil Compaction Beneath the Tractor Tire using Multilayer Perceptron Neural Networks
description	Introduction Soil compaction is one of the most destructive effects of machine traffic. Compaction increases soil mechanical strength and reduces its porosity, plant rooting and ultimately the yield. Nowadays, agricultural machinery has the maximum share on soil compaction in modern agriculture. The soil destruction may be as surface deformation or as subsurface compaction. Any way machine traffic destructs soil structure and as result has unfavorable effect on the yield. Hence, soil compaction recognition and its management are very important. In general, soil compaction is the most destructive effect of machine traffic. Modeling of ecological systems by conventional modeling methods due to the multitude effective parameters has always been challenging. Artificial intelligence and soft computing methods due to their simplicity, high precision in simulation of complex and nonlinear processes are highly regarded. The purpose of this research was the modeling of soil compaction system affected by soil moisture content, the tractor forward velocity and soil depth by multilayer perceptron neural network. Materials and Methods In order to carry out the field experiments, a tractor MF285 which was equipped with a three-tilt moldboard plough was used. Experiments were conducted at the Agricultural research field of University of Mohaghegh Ardabili in five levels of moisture content of 11, 14, 16, 19 and 22%, forward velocity of 1, 2, 3, 4 and 5 km/h, and soil depths of 20, 25, 30, 35 and 40 cm as a randomized complete block design with three replications. In this study, perceptron neural network with five neurons in the hidden layer with sigmoid transfer function and linear transfer function for the output neuron was designed and trained. Results and Discussion Field experiments showed three main factors were significant on the bulk density (P
format	article
author	Gh Shahgholi H Ghafouri Chiyaneh T Mesri Gundoshmian
author_facet	Gh Shahgholi H Ghafouri Chiyaneh T Mesri Gundoshmian
author_sort	Gh Shahgholi
title	Modeling of Soil Compaction Beneath the Tractor Tire using Multilayer Perceptron Neural Networks
title_short	Modeling of Soil Compaction Beneath the Tractor Tire using Multilayer Perceptron Neural Networks
title_full	Modeling of Soil Compaction Beneath the Tractor Tire using Multilayer Perceptron Neural Networks
title_fullStr	Modeling of Soil Compaction Beneath the Tractor Tire using Multilayer Perceptron Neural Networks
title_full_unstemmed	Modeling of Soil Compaction Beneath the Tractor Tire using Multilayer Perceptron Neural Networks
title_sort	modeling of soil compaction beneath the tractor tire using multilayer perceptron neural networks
publisher	Ferdowsi University of Mashhad
publishDate	2018
url	https://doaj.org/article/5f48000d690249c8bb7cb80d9aa621bc
work_keys_str_mv	AT ghshahgholi modelingofsoilcompactionbeneaththetractortireusingmultilayerperceptronneuralnetworks AT hghafourichiyaneh modelingofsoilcompactionbeneaththetractortireusingmultilayerperceptronneuralnetworks AT tmesrigundoshmian modelingofsoilcompactionbeneaththetractortireusingmultilayerperceptronneuralnetworks
_version_	1718429836456755200

Modeling of Soil Compaction Beneath the Tractor Tire using Multilayer Perceptron Neural Networks

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