Development of Experimental Setup for Distinction of Disease Plant

Matsuo, Kazuyuki; Nishiwaki, Kentaro; Zhang, Shuhuai; Yashiro, Mikio

doi:10.13031/2013.20856

2006 Portland, Oregon, July 9-12, 2006 2006

DOI: 10.13031/2013.20856

|View full text |Cite

Development of Experimental Setup for Distinction of Disease Plant

Kazuyuki Matsuo¹,

Kentaro Nishiwaki²,

Shuhuai Zhang³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2015

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Band Selection Using Forward Feature Selection Algorithm for Citrus Huanglongbing Disease Detection

Katti

Lee

Ehsani

et al. 2015

Journal of Biosystems Engineering

View full text Add to dashboard Cite

Purpose: This study investigated different band selection methods to classify spectrally similar data -obtained from aerial images of healthy citrus canopies and citrus greening disease (Huanglongbing or HLB) infected canopies -using small differences without unmixing endmember components and therefore without the need for an endmember library. However, large number of hyperspectral bands has high redundancy which had to be reduced through band selection. The objective, therefore, was to first select the best set of bands and then detect citrus Huanglongbing infected canopies using these bands in aerial hyperspectral images. Methods: The forward feature selection algorithm (FFSA) was chosen for band selection. The selected bands were used for identifying HLB infected pixels using various classifiers such as K nearest neighbor (KNN), support vector machine (SVM), naïve Bayesian classifier (NBC), and generalized local discriminant bases (LDB). All bands were also utilized to compare results. Results: It was determined that a few well-chosen bands yielded much better results than when all bands were chosen, and brought the classification results on par with standard hyperspectral classification techniques such as spectral angle mapper (SAM) and mixture tuned matched filtering (MTMF). Median detection accuracies ranged from 66-80%, which showed great potential toward rapid detection of the disease. Conclusions: Among the methods investigated, a support vector machine classifier combined with the forward feature selection algorithm yielded the best results.

show abstract

Band Selection Using Forward Feature Selection Algorithm for Citrus Huanglongbing Disease Detection

Katti

Lee

Ehsani

et al. 2015

Journal of Biosystems Engineering

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Development of Experimental Setup for Distinction of Disease Plant

Cited by 1 publication

References 0 publications

Band Selection Using Forward Feature Selection Algorithm for Citrus Huanglongbing Disease Detection

Band Selection Using Forward Feature Selection Algorithm for Citrus Huanglongbing Disease Detection

Contact Info

Product

Resources

About