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.
Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-4900 |
Date | 05 May 2007 |
Creators | Doshi, Rushabh Ashok |
Publisher | Scholars Junction |
Source Sets | Mississippi State University |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | Theses and Dissertations |
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