Artificial Neural Network Model for Predicting Specific Draft Force and Fuel Consumption Requirement of a Mouldboard Plough

A 2-(5-8)-2 artificial neural network (ANN) model, with a back propagation learning algorithm, was developed to predict specific draft force and fuel con-sumption requirements of mouldboard plough in a clay loam soil under varying operating conditions. The input parameters of the network were tillage depth and forward speed of operation. The output from the network were the specific draft force and fuel consumption requirement of the mouldboard plough. The developed model predicted the specific draft force and fuel consumption re-quirement of mouldboard plough with an error

PDF

___

Al-Hamed SA, Wahby MF, Al-Saqer SM, Aboukarima AM, Sayedahmed AA (2013). Artificial Neural Network Model for Predicting Draft and Energy Requirements of a Disk Plow. The Journal of Ani-mal and Plant Sciences, 23(6): 1714-1724

Al-Janobi AA, Aboukarima AM, Ahmed KA (2001). Prediction of specific draft of different tillage im-plements using neural network. MISR Journal of Agricultural Engineering, 18(3): 699-714.

Altinisik A (2012). Analysıs Of Tractor Traction Per-formance Wıth Artificial Neural Networks In Culti-vation. The Graduate School Of Natural And Ap-plıed Scıence Of Selçuk Unıversıty, Doctora The-sis. (In Turkish).

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 va-pour-compression liquid chillers: artificial neural networks. Appl Therm Eng, 21, 941-53.

Bentaher H, Ibrahmi A, Hamza E, Hbaieb M, Kantchev G, Maalej A, Arnold W (2013). Finite element sim-ulation of moldboard–soil interaction. Soil and Till-age Research 134:11–16

Godwin RJ, O’Dogherty MJ, Saunders S, Balafoutis AT (2007). A force prediction model for mould-board ploughs incorporating the effects of soil characteristic properties, plough geometric factors and ploughing speed. Biosystems Engineering, 97, 117–129.

Godwin RJ, O’Dogherty MJ (2007). Integrated soil tillage force prediction models. Journal of Terrame-chanics, 44: 3–14.

Jacobs RA (1988). Increased Rate of Convergence Through Learning Rate Adaptation. Neural Net-works, 1(4): 295-307.

Karparvarfard SH, Rahmanian-Koushkaki H (2015). Development of a fuel consumption equation: Test case for a tractor chisel-ploughing in a clay loam soil. Biosystems Engineering 130, 23-33.

Khalilian A, Batchelder DG, Self K, Summers JD (1984). Revision of fuel consumption equation for diesel tractors. ASAE Paper No. 84-1522.

Kheiralla AF, Yahya A, Zohadie M, Ishak W (2004). Modeling of power and energy requirements for tillage implements operating in sandy clay loam, Malaysia. Soil and Tillage Research, 78, 21–34.

Levenberg K (1944). A Method For the Solution of Certain Nonlinear Problems in Least Squares. Quart. Appl. Math., 2, 164-168.

Mamkagh AM (2019). Effect of Soil Moisture, Tillage Speed, Depth, Ballast Weight and, Used Implement on Wheel Slippage of the Tractor: A Review. Asian Journal of Advances in Agricultural Research 9(1): 1-7.

Marquardt DW (1963). An Algorithm For Least-Squares Estimation Of Nonlinear Parameters. J. Soc. Indust. Appl. Math., 11, 431-441.

Minai AA, Williams RD (1990). Back-Propagation Heuristics: a Study of The Extended Delta-bar-delta Algorithm” International Joint Conference on Neu-ral Networks, vol.1, 595-600, San Diego, CA, USA.

Opara-Nadi OA (2008). Direct planting for increased crop production. www.fao.org/ag/ags/AGSE/7mo/69/chapter8.pdf

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 require-ment of a tillage tool in a soil bin using artificial neural network. ASABE Paper No. 111112. Ameri-can Society of Agricultural and Biological Engi-neers.

Roul AK, Raheman H, Pansare MS, Machavaram R, (2009). Predicting the draught requirement of till-age implements in sandy clay loam soil using an ar-tificial neural network. Biosystems Engineering 104(4), 476-485.

Sahu RK, Raheman H (2006). Draught prediction of agricultural implements using reference tillage tools in sandy clay loam soil. Biosystems Engineering, 94(2), 275–284.

Taylor JH (1980). Energy savings through improved tractive efficiency. ASAE 4-81.

Upadhyaya SK, Williams TH, Kemble LJ, Collins NE (1984). Energy requirements for chiseling in coastal plane soils. Transaction of the ASAE, 27(6): 1643-1649.

Visen NS, Paliwal J, Jayas DS, White NDG (2002). Specialist neural networks for cereal grain classifi-cation. Biosystems Engineering 82, 151-159.

Zhang ZX, Kushwaha RL (1999). Applications of neural networks to simulate soil-tool interaction and soil behavior. Canadıan Agrıcultural Engineering 41(2), 119-125.