Predicting and Optimizing Tillage Draft Using Artificial Network Technique
Shehta, Y., Awady, N., Kabany, A., Abd-Elwahed, M., Elhelew, W. (2023). Predicting and Optimizing Tillage Draft Using Artificial Network Technique. EKB Journal Management System, 31(1), 15-28. doi: 10.21608/ajs.2023.171425.1500
Yasmin Shehta; Nabil Awady; Abdel-Fadil Kabany; Mohammed Abd-Elwahed; Waleed Elhelew. "Predicting and Optimizing Tillage Draft Using Artificial Network Technique". EKB Journal Management System, 31, 1, 2023, 15-28. doi: 10.21608/ajs.2023.171425.1500
Shehta, Y., Awady, N., Kabany, A., Abd-Elwahed, M., Elhelew, W. (2023). 'Predicting and Optimizing Tillage Draft Using Artificial Network Technique', EKB Journal Management System, 31(1), pp. 15-28. doi: 10.21608/ajs.2023.171425.1500
Shehta, Y., Awady, N., Kabany, A., Abd-Elwahed, M., Elhelew, W. Predicting and Optimizing Tillage Draft Using Artificial Network Technique. EKB Journal Management System, 2023; 31(1): 15-28. doi: 10.21608/ajs.2023.171425.1500

Predicting and Optimizing Tillage Draft Using Artificial Network Technique

Arab Universities Journal of Agricultural Sciences
Article 24, Volume 31, Issue 1, June 2023, Page 15-28  XML PDF (529.48 K)
Document Type: Original Article
DOI: 10.21608/ajs.2023.171425.1500
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Authors
Yasmin Shehta email orcid 1; Nabil Awady1; Abdel-Fadil Kabany1; Mohammed Abd-Elwahedorcid 2; Waleed Elheleworcid 1
1Agricultural Engineering Department, Faculty of Agriculture, Ain Shams University, Cairo, Egypt.
2Soil Sci dept., Faculty of Agric., Ain Shams Univ., Cairo Egypt
Abstract
Tillage as one of the agricultural practices consumes the largest amount of energy, which reflects on the total production cost. The artificial neural network (ANN) technique was utilized in the current study to opti-mize the performance of the tillage process. The ANN-modeled multilayer perceptron network with a backpropagation learning algorithm and momen-tum term was used by the PYTHON program. The ANN inputs were: the implement type, soil texture, moisture, bulk density, width, speed, and depth. The draught was the output (kN). Five layers composed the ANN model's optimal configuration (13-64-16-4-1). The linear and rectified linear units (ReLU) functions were utilized with hidden layers and the output layer, re-spectively. Momentum term and learning rate were 0.00003 and 0.9 respec-tively. The iteration number was 1000 epochs and stopped at 290 epochs. The coefficient of determination in the test datasets was high (0.92) while the difference between actual and predicted output was low (2.08). Bulk den-sity and depth were the main determinants of the draft. The evaluation of the developed model for chisel, moldboard, and disk plow gave satisfactory re-sults of 0.985, 0.924, and 0.917. In comparison to the ANNs, the regression model's correlation coefficient for predicting draught force was the lowest (0.373).
Keywords
Machine learning models; Artificial neural network; Tillage performance; Energy needs; draught
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