Kazım ÇARMAN, Tamer MARAKOĞLU, Alper TANER, Ergün ÇITIL

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

One 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.

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Abbaspour-Gilandeh Y, Alimardani R, Khalilian A, Keyhani AR, Sadati SH (2008). The prediction of draft force and energy required for subsoiling operation using artificial neural network model. Fifth national conference of agricultural machinery and mechanization Engineering, Iran

Abbaspour-Gilandeh Y, Fazeli M, Roshanianfard A, Hernández-Hernández M, Gallardo-Bernal I, Hernández-Hernández JL (2020). Prediction of Draft Force of a Chisel Cultivator Using Artificial Neural Networks and Its Comparison with Regression Model. Agronomy, 10,451, 1-15.

Aboukarima AWM (2007). Draft models of chısel plow based on sımulatıon usıng artıfıcıal neural networks. Farm Machınery and Power, 24(1): 42-61.

Al-Janobi AA, Aboukatima AM, Ahmed KA (2001). Prediction of Specific Draft of Different Tillage Implements Using Neural Networks. Misr Journal of Agricultural Engineering, 18 (3): 699-714.

Al-Suhaibani SA, Ghaly AE (2013). Comparative Study of the Kinetic Parameters of Three Chisel Plows Operating at Different Depths and Forward Speed in A Sandy Soil. The International Journal of Engineering and Science, 2(7), 42-59.

Akbarnia A, Mohammadi A, Farhani F, Alimardani R (2014). Simulation of draft force of winged share tillage tool using artificial neural network model.Agricultural Engineering International: the CIGR Journal, 16(4), 57-65.

Anderson G (2009). The impact of tillage practices and crop residue (stubble) retention in the cropping system of Western Australia. Bulletin Number 4786, ISSN: 1833-7236.

Armin A (2014). Mechanics of soil-blade interaction. PhD Thesis, Department of Mechanical Engineering University of Saskatchewan, Saskatoon. Askari M, Shahgholi GH, Abbaspour-Gilandeh Y (2017). The effect of tine, wing, operating depth and speed on the draft force of subsoil tillage tines. Prog. Agric. Eng.Sci. 63, 160

Askari M, Abbaspour-Gilandeh Y (2019). Assessment of adaptive neuro-fuzzy inference system and response surfacemethodology approaches in draft force prediction of subsoiling tines. Soil and Tillage Research, 194, 1-8.

Bağırkan Ş (1993). İstatistiksel Analiz. Bilim Teknik Yayınevi. s. 301. İstanbul.

Bechtler H, Browne MW, Bansal PK, Kecman V (2001). New approach to dynamic modelling of vapour-compression liquid chillers: artificial neural networks. Appl Therm Eng, 21, 941-53.

Boydas MG (2004). Effect of different structural properties of some primary soil tillage implements used ın wheat production on soıl physical properties, field capacity, drawbar power and fuel consumption. PhD Thesis, Department of Agricultural Machinery, University of Atatürk, Erzurum (In Turkish).

Boydas MG (2017). Determination of effect of different wing mouth forms, different travelling speeds and different working depths on draft force in wings used in winged chisel plough. Medıterranean Agrıcultural Scıences, 30(3), 219-225 (In Turkish)

Çarman K, Marakoglu T, Taner A, Mikailsoy F (2016). Measurements and modelling of wind erosion rate in different tillage practices using a portable wind erosion tunnel. Zemdirbyste-Agriculture, 103 (3), 327- 334.

Çarman K, Çıtıl E, Taner A (2019). Artificial Neural Network Model for Predicting Specific Draft Force and Fuel Consumption Requirement of a Mouldboard Plough. Selcuk Journal of Agriculture and Food Sciences, 33 (3), 241-247.

Fawcett R, Towery D (2005). Conservation Tillage and Plant Biotechnology: How New Technologies Can Improve the Environment By Reducing the Need to Plow. Conservation Technology Information Center, West Lafayette, USA.

Godwin R J, O’Dogherty MJ (2007). Integrated soil tillage force prediction models. Journal of Terramechanics, 44, 3–14.

Karmakar S (2005). Numerical modelling of soil flow and pressure distribution on a simple tillage tool using computational fluid dynamics. PhD Thesis, Department of Agricultural and Bioresource Engineering, University of Saskatchewan, Canada.

Kees G (2008). Using subsoiling to reduce soil compaction. USDA Forest Service Technology and Development Program Missoula, MT. http://www.fs.fed.us/t/pubs/pdfpubs/pdf08342828/ pdf08342828dpi72.pdf.

Manuwa SI (2009). Performance evaluation of tillage tines operating under different depths in a sandy clay loam soil. Soil and Tillage Research, 103, 399–405.

Manuwa SI, Ademosun OC, Agbetoye LAS, Adesına A (2010). Soil bin ınvestigations of the effects of tillage tool width on draught and soil dısturbance parameters in sandy clay loam soil. XVII. World Congress of the International Commission of Agricultural and Biosystems Engineering (CIGR), 1-9.

Marakoğlu T, Çarman K, Çitil E (2013). The effect on soil deformation of different type chisel share. Journal of Agricultural Machinery Science, 9(3), 175- 179 (In Turkish).

Neisy I (2014). Introduction to the study of soil-tool modelling. Walia journal, 30(1), 31-34.

Purushothaman S, Srinivasa YG (1994). A back-propagation algorithm applied to tool wear monitor-ing. International Journal of Machine Tools and Manufacture, 34(5): 625-631.

Rahman A, Kushawaha RL, Ashrafizadeh SR, Panigrahi S (2011). Prediction of energy requirement of a tillage tool in a soil bin using artificial neural network. ASABE Paper No. 111112. USA.

Salar MR, Esehaghbeygi A, Hemmat A (2013). Soil loosening characteristics of a dual bent blade subsurface tillage implement. Soil and Tillage Research 134:17-24.

Simmons FW, Nafziger ED (2010). Soil Management and Tillage. Illinois Agronomy Handbook, pp.133- 142.

Scott BJ, Eberbach PL, Evans J, Wade LJ (2010). Stubble retention in cropping systems in Southern Australia: Benefits and Challenges. Industry and Investment NSW, ISBN: 9781 74256 020 5.

Suzuki K (2013). Artificial Neural Networks: Architectures and Applications; InTech Publisher: Rijeka, Croatia.

Topakcı M (2004). A study on the improvement of soil failure area in tillage by chisel tine. PhD Thesis, Department of Agricultural Machinery, University of Akdeniz, Antalya (In Turkish).

Zadeh SRA (2006). Modelling of energy requirements by A narrow tillage tool. PhD Thesis, Department of Agricultural and Bioresource Engineering University of Saskatchewan, Saskatoon.