Skip to main content
ARS Home » Plains Area » College Station, Texas » Southern Plains Agricultural Research Center » Aerial Application Technology Research » Research » Publications at this Location » Publication #323876
Title: Combining fuzzy set theory and nonlinear stretching enhancement for unsupervised classification of cotton root rot

Author
item SONG, H - Northwest Agricultural & Forestry University
item Yang, Chenghai
item ZHANG, J - Huazhong Agricultural University
item HE, D - Northwest Agricultural & Forestry University
item THOMASSON, J - Texas A&M University

Submitted to: Journal of Applied Remote Sensing (JARS)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/20/2015
Publication Date: 12/30/2015
Citation: Song, H., Yang, C., Zhang, J., He, D., Thomasson, J.A. 2015. Combining fuzzy set theory and nonlinear stretching enhancement for unsupervised classification of cotton root rot. Journal of Applied Remote Sensing (JARS). 9:096013.

Interpretive Summary: Cotton root rot is a destructive disease affecting cotton production. The uncertainties caused by various infection stages and newly infected plants make it difficult to achieve accurate classification results from airborne imagery. This research used applied image enhancement techniques to airborne imagery for identification of cotton root rot infections. Imagery was first enhanced using enhancement algorithms and then classified into infected and non-infected areas. The results showed that image enhancement has improved the classification accuracy of two classification methods for four study fields. The results from this study will be useful for accurate detection of cotton root rot and for site-specific treatment of the disease.

Technical Abstract: Cotton root rot is a destructive disease affecting cotton production. Accurate identification of infected areas within fields is useful for cost-effective control of the disease. The uncertainties caused by various infection stages and newly infected plants make it difficult to achieve accurate classification results from airborne imagery. The objectives of this study were to apply fuzzy set theory and nonlinear stretching enhancement to airborne multispectral imagery for unsupervised classification of cotton root rot infections. Four cotton fields near Edroy and San Angelo, Texas, were selected for this study. Airborne multispectral imagery was taken and the color-infrared (CIR) composite images were used for classification. The intensity component was enhanced by using a fuzzy-set based method, and the saturation component was enhanced by a nonlinear stretching image enhancement algorithm. The enhanced CIR composite images were then classified into infected and noninfected areas. Iterative self organization data analysis and adaptive Otsu’s method were used to compare the performance of the proposed image enhancement method. The results showed that image enhancement has improved the classification accuracy of these two unsupervised classification methods for all four fields. The results from this study will be useful for detection of cotton root rot and for site-specific treatment of the disease.