Artificial Neural Network Approach for the Prediction of the Corn (Zea Mays L.) Leaf Area

Abstract

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.