Advisor

King, Roger L.

Date of Degree

1-1-2007

Document Type

Graduate Thesis - Open Access

Degree Name

Master of Science

College

College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

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.

URI

https://hdl.handle.net/11668/18180

Comments

Rotylenchulus reniformis||Nematode||Prediction||Classification||Cotton||Hyperspectral||Self-Organizing Maps (SOM)

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