| Titles |
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English :
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Plows Performance under Egyptian conditions Depicted by Artificial Neural Networks
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Arabic :
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وصف أداء المحاريث تحت الظروف المصرية بالشبكات العصبية الإصطناعية.
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| Abstract |
Multilayer feedforward neural network with 12 input and 4 output neurons was trained using a backpropagation learning algorithm. It was used to depict and predict plows performance during plowing process under Egyptian conditions. Data needed to train and test the artificial neural network (ANN) model was obtained from previous similar field experiments found in literatures. The obtained data set were 430 data points where 320 and 110 of them were used for training and testing ANN model respectively. The input parameters were soil texture index, chisel plow, moldboard plow, disc plow, rotary plow, plowing depth, rated width of plow, forward speed, initial soil moisture content, initial soil bulk density, rated tractor power and number of plow passes over the soil. The results showed that the variation between observed and predicted plows performance parameters was small and the coefficients of determination (R2) were 0.86, 0.74, 0.80 and 0.79 for effective field capacity (fed/h), fuel consumption (lit/h and lit/fed) and plowing energy (kW.h/fed) respectively. Verification of the ANN model in prediction was conducted using field data for chisel plow operating at three forward speeds and one plowing depth. The results indicated that the ANN model was able to predict plows performance parameters and the trends of the predicted results of fuel consumption and plowing energy with forward speed were the same as published in local previous studies for chisel plow.
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| Publication year |
2003
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| Pages |
936 - 919
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| Availability location |
مكتبة كلية الزراعة - جامعة الازهر
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| Availability number |
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| Organization Name |
Agricultural Engineering Research Institute (AENRI)
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| Country |
Egypt
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| City |
القاهرة
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| Publisher |
Name:
الجمعية المصرية للهندسة الزراعية
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| serial title |
المجلة المصرية للهندسة الزراعية عدد أكتوبر2003
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| Volume |
20
. (4)
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| Department |
Agriculture Power and Energy
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| Author(s) from ARC |
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| External authors (outside ARC) |
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| Agris Categories |
Agricultural machinery and equipment
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AGROVOC
TERMS |
Neural networks.
Ploughs.
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| Publication Type |
Journal
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