Self-organizing maps for classification and prediction of nematode populations in cotton.

摘要

In this work, different Rotylenchulus reniformis nematode population numbers affecting cotton plants were spectrally classified using Self-Organized Maps. The hyperspectral reflectance of cotton plants affected by different nematode population numbers were analyzed in order to extract information from the signal that would lead to a fieldworthy methodology for predicting nematode population numbers extant in a plant's rhizosphere. Hyperspectral reflectances from both control and field nematode infestations were used in this work. Various feature extraction and dimensionality reduction methods (e.g., PCA, DWT, and SOM-based methods) were used to extract a reduced set of features. These extracted features were then classified using a supervised SOM classification method. Additionally, this work explores the possibility of combining the standard feature extraction methods with self-organized maps to extract a reduced set of features in order to increase classification accuracies.