Artificial Neural Network Approach for the Predicition of the Corn (Zea mays L.) Leaf Area

M. S. ODABAS¹, E. ERGUN² and F. ONER^3
1 Ondokuz Mayis University, Bafra Vocational School, Bafra, Samsun, Turkey
³ Ondokuz Mayis University, Faculty of Engineering, Department of Computer Engineering, Samsun,Turkey
³ Ordu University, Faculty of Agriculture, Department of Field Crops, Ordu, Turkey

Abstract

ODABAS, M. S., E. ERGUN and F. ONER, 2013. Artificial neural network approach for the predicition of the corn (Zea mays L.) leaf area. Bulg. J. Agric. Sci., 19: 766-769

 

This research investigates the artificial neural networks utilization in improving leaf area forecasting at corn leaves (Zea mays L.). Best fitting results were obtained with 2 input nodes (leaf length and leaf width), 2 hidden layers and one output (leaf area). Artificial neural network model performance was tested successfully to describe the relationship between actual leaf area and predicted leaf area. R2 of leaf area was 0.98. Artificial neural networks model produced satisfied correlation between measured and predicted value and minimum inspection error.

Key words: Artificial neural network, Corn, Leaf area, Modeling, Zea mays L.
Abbreviations: ANNs-Artificial neural networks, L- leaf length, LA-Leaf area, W-leaf width

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