A plant's reflectance can vary significantly depending on the type of stressors affecting it. Parasitic nematode species such as Meloidogyne incognita and Rotylenchulus reniformis are two of the leading nematode species affecting cotton plants. There is a need to detect the type of nematode in order to start proper nematode management program. Use of remotely sensed hyperspectral data could be one of the choices for species identification but, remotely sensed hyperspectral data are usually associated with high dimensions and requires some sort of dimensionality reduction without losing vital information. Some of the standard feature extraction and dimensionality reduction methods widely used nowadays are DWT and Self-Organized Maps (SOM) based methods. In this paper, authors explore the possibility of combining two above mentioned feature extraction and dimensionality reduction methods to extract feature for better classification accuracies pertaining to this study. The accuracies were then compared with the accuracies obtained using features extracted from DWT and SOM-based methods separately.For the entire analysis, SOM-supervised classification method was used.
Rotylenchulus reniformis nematodes present in the soil are one of the major nematode parasite species significantly affecting the growth and development of cotton plants. Recent studies have shown that the nematode numbers in the plant's rhizosphere has direct impact on the reflectance of the plants. In this paper, authors utilize this correlation in developing a field worthy methodology for predicting nematode population number extant in the plant's rhizosphere from variable plant's reflectance. To accomplish this task, a supervised Self-organized map (SOM) was trained using the hyperspectral data signatures of cotton plants affected by different known nematode numbers. The hyperspectral signatures used for training were collected from the cotton plants grown in controlled environment. Twelve field samples (uncontrolled environment) with known nematode numbers obtained from lab analysis of the soil were presented to the supervised trained Self-Organized Map. The location of the sample on the labeled supervised-SOM was used to determine the estimated nematode population of the field sample. In addition to the map grid, the locations of the samples were also visualized using U-matrix, to determine whether the samples were not corrupt or located in the junk part of the map. In addition to the primary goal, hyperspectral signatures of both training and testing data were divided into three sub-regions: Visible region, NIR region and Mid-IR region to observe whether any particular region was the most effective in predicting nematode population.
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