Prediction of Draft Force and Disturbed Soil Area of a Chisel Tine in Soil Bin Conditions Using Draft Force and Its Comparison with Regression Model

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dc.authorid0000-0002-9860-7403en_US
dc.authorid0000-0002-2824-116Xen_US
dc.authorid0000-0001-8679-2069en_US
dc.authorid0000-0003-2023-9409en_US
dc.contributor.authorÇarman, Kazım
dc.contributor.authorMarakoğlu, Tamer
dc.contributor.authorTaner, Alper
dc.contributor.authorÇıtıl, Ergün
dc.date.accessioned2023-08-03T10:04:55Z
dc.date.available2023-08-03T10:04:55Z
dc.date.issued2021en_US
dc.departmentSelçuk Üniversitesi, Ziraat Fakültesi, Tarım Makinaları ve Teknolojileri Mühendisliği Bölümüen_US
dc.description.abstractOne of our most valuable natural resources is soil. Sustainable agricultural production is achieved with proper soil management. Tillage is considered to be one of the largest operations, as the most energy need in agricultural production occurs in tillage. The main purpose of this study is to investigate the effects of chisel tine on draft force and disturbed soil area and estimate them using artificial neural networks (ANN) and multiple linear regression equations (MLR). The experiments were carried out in a closed soil bin filled with clay loam soil at an average moisture content of 13.2% (on dry basis). The draft force and disturbed soil area were evaluated as affected by the share width at two levels (60 and 120 mm), forward speed at four levels (0.7, 1, 1.25 and 1.5 ms-1 ) and working depth at four levels (160, 200, 240 and 280 mm) at three replications. The draft force varied from 0.5 to 1.42 kN, depending on the controlled variables, while the disturbed soil area varied from 260 to 865 cm2 . Test results show that share width, forward speed and working depth were significant on the draft force and disturbed soil area. Input variables of the ANN models were considered share width, forward speed and working depth. In prediction of required draft force and disturbed soil area respectively, on account of statistical performance criteria, the best ANN model with coefficient of determination of 0.999 and 0.998, root mean square error of 0.010 and 0.016 and mean relative percentage error of 0.960 and 1.673 was better performed than the MLR model.en_US
dc.identifier.citationÇarman, K., Marakoğlu, T., Taner, A., Çıtıl, E., (2021). Prediction of Draft Force and Disturbed Soil Area of a Chisel Tine in Soil Bin Conditions Using Draft Force and Its Comparison with Regression Model. Selcuk Journal of Agriculture and Food Sciences, 35(1), 56-64. DOI: 10.15316/SJAFS.2020.229en_US
dc.identifier.doi10.15316/SJAFS.2020.229en_US
dc.identifier.endpage64en_US
dc.identifier.issn2458-8377en_US
dc.identifier.issue1en_US
dc.identifier.startpage56en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/49126
dc.identifier.volume35en_US
dc.institutionauthorÇarman, Kazım
dc.institutionauthorMarakoğlu, Tamer
dc.institutionauthorÇıtıl, Ergün
dc.language.isoenen_US
dc.publisherSelçuk Üniversitesien_US
dc.relation.ispartofSelcuk Journal of Agriculture and Food Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectDraft Forceen_US
dc.subjectDisturbed Soil Areaen_US
dc.subjectChisel Tineen_US
dc.titlePrediction of Draft Force and Disturbed Soil Area of a Chisel Tine in Soil Bin Conditions Using Draft Force and Its Comparison with Regression Modelen_US
dc.typeArticleen_US

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