Pruning neural network for architecture optimization applied to near-infrared reflectance spectroscopic measurements. Determination of the nitrogen content in wheat leaves

The pruning neural network, based on the algorithm called optimum brain sur geon, was used for network architecture optimization. This network pruning procedure was applied for estimating the nitrogen contents in wheat leaves, using near-infrared diffuse reflectance spectroscopy. The results obtained with pruning were compared with those obtained by using ordinary procedure s with neural networks, partial least squares, polynomial partial least squ ares and neural networks/partial least squares methodologies. Comparison of the results with those obtained by the conventional Kjeldahl method showed that the results with pruning neural networks were as good as those with o rdinary neural networks and with PLS/neural networks, but better than those with the other methodologies. Although the comparison was performed for on e data set, the pruning procedure has the advantage of introducing an autom atic architecture optimization, which is cumbersome when performed by the o ther neural network procedures used in this work, generating a simplified m odel with better generalization abilities.