Predicting Unit Draft of Tillage Implements Using Statistical Models and Neural Net Works
Abstract: A unit draft of tillage implements was predicted using statistical and neural networks models. The neural network was a multilayer feedforward network with 11 input and 1 output neurons. The input variables were chisel plow, moldboard plow, disc plow, soil texture index, plowing depth, rated plow width, forward speed, initial soil moisture content, initial soil bulk density, rated tractor power, and the number of plow passes over the soil. The neural network was trained using backpropagation learning algorithm. The overall performance of the neural network was quite sufficient. It could be used to predict the unit draft of tillage implement trends of the measured data for all plows. The standard deviations of the errors were 9.38, 6.57,and 8.45 kN/m2 for moldboard, chisel, and disc plows, respectively. Also, the coefficients of the linear correlation between the measured and the predicted values were higher than 0.95 for both the neural network and statistical models.
Publication year 2004
Pages 249 - 239
Availability location مكتبة كلية الزراعة جامعة الازهر
Availability number
Organization Name
Country Egypt
City القاهرة
Publisher Name: الجمعية المصرية للهندسة الزراعية
serial title بمؤتمر الجمعية المصرية للهندسة الزراعية الثانى عشر مع قسم الهندسة الزراعية بكلية الزراعة جامعة الأسكندرية فى 4-5 أكتوبر 2004
Department Agriculture Power and Energy
Author(s) from ARC
External authors (outside ARC)
    محمد نبيل العوضى
    عبد الفضيل جابر القبانى
Agris Categories Agricultural machinery and equipment
AGROVOC
TERMS		 Models. Tillage. Tillage equipment.
Proposed Agrovoc draft force;
Publication Type Conference/Workshop
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