DocumentCode :
2663835
Title :
Self-organized maps based spectral prediction of Rotylenchulus reniformis numbers
Author :
Doshi, Rushabh A. ; King, Roger L. ; Lawrence, Gary W.
Author_Institution :
Mississippi State Univ., Starkville
fYear :
2007
fDate :
23-28 July 2007
Firstpage :
444
Lastpage :
447
Abstract :
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.
Keywords :
agriculture; geophysical signal processing; geophysical techniques; image processing; learning (artificial intelligence); microorganisms; self-organising feature maps; vegetation; NIR region hyperspectral data; Rotylenchulus reniformis; SOM training; U-matrix; cotton plant hyperspectral data; cotton plants; mid IR region hyperspectral data; nematode parasite species; nematode population prediction; plant reflectance; plant rhizosphere; self organized maps; soil nematodes; visible region hyperspectral data; Cotton; Crops; Data visualization; Hyperspectral imaging; Pathology; Reflectivity; Soil; Spectroradiometers; Spraying; Testing; Hyperspectral; Nematode; Rotylenchulus reniformis; Self-Organized Maps;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-1211-2
Electronic_ISBN :
978-1-4244-1212-9
Type :
conf
DOI :
10.1109/IGARSS.2007.4422826
Filename :
4422826
Link To Document :
بازگشت