A NEURO-FUZZY FRAMEWORK FOR ASSESSMENT SOIL COMPACTION DUE TO TRAFFIC OF AGRICULTURAL IMPLEMENTS ON DfF'FERENT SOILS
Elbanna, E., Aboukarima, A., Mourad, R. (2005). A NEURO-FUZZY FRAMEWORK FOR ASSESSMENT SOIL COMPACTION DUE TO TRAFFIC OF AGRICULTURAL IMPLEMENTS ON DfF'FERENT SOILS. EKB Journal Management System, 30(9), 5365-5379. doi: 10.21608/jssae.2005.237771
E. Elbanna; A. M. Aboukarima; R. I. A. Mourad. "A NEURO-FUZZY FRAMEWORK FOR ASSESSMENT SOIL COMPACTION DUE TO TRAFFIC OF AGRICULTURAL IMPLEMENTS ON DfF'FERENT SOILS". EKB Journal Management System, 30, 9, 2005, 5365-5379. doi: 10.21608/jssae.2005.237771
Elbanna, E., Aboukarima, A., Mourad, R. (2005). 'A NEURO-FUZZY FRAMEWORK FOR ASSESSMENT SOIL COMPACTION DUE TO TRAFFIC OF AGRICULTURAL IMPLEMENTS ON DfF'FERENT SOILS', EKB Journal Management System, 30(9), pp. 5365-5379. doi: 10.21608/jssae.2005.237771
Elbanna, E., Aboukarima, A., Mourad, R. A NEURO-FUZZY FRAMEWORK FOR ASSESSMENT SOIL COMPACTION DUE TO TRAFFIC OF AGRICULTURAL IMPLEMENTS ON DfF'FERENT SOILS. EKB Journal Management System, 2005; 30(9): 5365-5379. doi: 10.21608/jssae.2005.237771

A NEURO-FUZZY FRAMEWORK FOR ASSESSMENT SOIL COMPACTION DUE TO TRAFFIC OF AGRICULTURAL IMPLEMENTS ON DfF'FERENT SOILS

Journal of Soil Sciences and Agricultural Engineering
Article 2, Volume 30, Issue 9, September 2005, Page 5365-5379  XML PDF (154.63 K)
Document Type: Original Article
DOI: 10.21608/jssae.2005.237771
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Authors
E. Elbanna1; A. M. Aboukarima2; R. I. A. Mourad3
1Agrtc. Eng. Dept., Faeulty 6f Agrlc., Mansoura Unlv.
2Agrle. Eng. Res. Inst., Agrle. Res. Center, MinIstry of Agrle. and Land Reclamation
3Agrie. Eng. Dept., Faculty of Agrlc., Fayoum Univ.
Abstract
In this research work Adaptive Neuro-Fuuy Inference System (ANFIS) in the
frame of Ma!lab was applied to develop a tool for soil compaction assessment that
may help planning of mechanized operations in order to increase the sustainability of
agricultural activily, The parameters of the triangular membership functions and the
zero-order Sugeno fuzzy model were computed by means of hybrid learning, The
model considers soil moisture content, clay ratio (represents soil type), wheel index
and the number of wheel passes over the field as input variables. The model has
been built based on field experimental data, The predicted index of soil compaction
was compared with measured values. The results demonstrate that neuro-fuzzy
modet deri ..•• ed by the proposed framework delivered satisfactory outcome in spite of the significant complexity of the considered problem.
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