Neural Network Optimisation of Remotely Sensed Maize Leaf Nitrogen with a Genetic Algorithm and Linear Programming using Five Performance Parameters

An algorithm was developed to select an optimum model among several neural network (NN) models using the Manhattan and Euclidean metric measures. The algorithm was implemented to find an optimum NN prediction model based on simultaneous comparison of five performance parameters. Weighted coefficients were given to each performance parameter based on their significance for specific condition. The associated weighted coefficients were optimised using two optimisation techniques: (i) genetic algorithm; and (ii) linear programming. The algorithm performed satisfactorily in determining acceptable models and selecting an optimum NN model. The radial basis function NN model based on green vegetation index texture yielded an average prediction accuracy of 92·1% for predicting leaf nitrogen content under field conditions.