Abstract
A new neural network architecture for classification purposes is proposed. The Self-Organizing Map (SOM) neural network is used in a supervised way for a classification task. The neurons of the SOM become associated with local linear mappings (LLM). 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 different kinds of weeds by using spectral reflectance measurements. The classification performance of the proposed method is proven superior compared to other neural classifiers. Also, the proposed method compares favorably with the results obtained by using an optimal Bayesian classifier.
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Moshou, D., Ramon, H. & De Baerdemaeker, J. A Weed Species Spectral Detector Based on Neural Networks. Precision Agriculture 3, 209–223 (2002). https://doi.org/10.1023/A:1015590520873
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DOI: https://doi.org/10.1023/A:1015590520873