Dergiler / Selcuk Journal of Agriculture and Food Sciences / 2019 / Cilt: 33 - Sayı: 3

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

Sayfa
241–247
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Further work is required to demonstrate the generalised value of this ANN in other soil conditions.

📌 3 Maddede Bu Makale

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Amaç & Hipotez: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.
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Temel Bulgular & Veri:The developed model predicted the specific draft force and fuel consumption re-quirement of mouldboard plough with an error <1 % when compared to the measured draft force and fuel consumption values.
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Sonuç & Katkı:Further work is required to demonstrate the generalised value of this ANN in other soil conditions.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Özet

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 <1 % when compared to the measured draft force and fuel consumption values. Such encouraging results indicate that the developed ANN model for specific draft force and fuel con-sumption requirement prediction could be considered as an alternative and practical tool for predicting draft force and fuel consumption requirement of tillage implements under the selected experimental conditions in clay loam soils. Further work is required to demonstrate the generalised value of this ANN in other soil conditions.

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Bu Makaleye Atıf Yap

ÇARMAN, K., ÇITIL, 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, Cilt: 33 - Sayı: 3, 241–247. https://ajindex.com/articles/340182

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Kaynakça (15)

  1. 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
  2. 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.
  3. 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.
  4. 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.
  5. 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
  6. 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.
  7. Godwin RJ, O’Dogherty MJ (2007). Integrated soil tillage force prediction models. Journal of Terrame-chanics, 44: 3–14.
  8. Jacobs RA (1988). Increased Rate of Convergence Through Learning Rate Adaptation. Neural Net-works, 1(4): 295-307.
  9. 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.
  10. Khalilian A, Batchelder DG, Self K, Summers JD (1984). Revision of fuel consumption equation for diesel tractors. ASAE Paper No. 84-1522.
  11. 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.
  12. Levenberg K (1944). A Method For the Solution of Certain Nonlinear Problems in Least Squares. Quart. Appl. Math., 2, 164-168.
  13. Mamkagh AM (2019).
  14. 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.
  15. Marquardt DW (1963). An Algorithm For Least-Squares Estimation Of Nonlinear Parameters. J.