Computers and electronics in agriculture vol:31 issue:1 pages:5-16
The Self-Organizing Map (SOM) neural network is used in a supervised way for a classification task. The neurons of the SOM are extended with local linear mappings. Error information obtained during training is used in a novel learning algorithm to train the classifier. The proposed method achieves fast convergence and good generalization. The classification method is then applied in a precision farming application, the classification of crops and weeds using spectral properties. The proposed method compares favorably with an optimal Bayesian classifier that is presented in the form of a probabilistic neural network. The classification performance of the proposed method is proven superior compared with other statistical and neural classifiers. (C) 2001 Elsevier Science B.V. All rights reserved.