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Title:Image-Based Analysis of Fungal-Damaged Soybeans
Author(s):Ahmad, Irfan Saleem
Doctoral Committee Chair(s):Reid, John F.
Department / Program:Agricultural Engineering
Discipline:Agricultural Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Computer Science
Abstract:Each of the three feature sets, color, morphology, and texture were able to discriminate specific seeds with varying degrees of success. A neuro-fuzzy inference system was developed to classify asymptomatic, Cercospora spp., and Fusarium spp. The classification accuracy for asymptomatic seed was 91.6%, Cercospora spp. 68%, and Fusarium spp. 95%. A multimedia computer-based soybean visual information and grading system was developed. The research concluded that fungal-damaged soybean seeds can be characterized based on their images.
Issue Date:1997
Type:Text
Language:English
Description:240 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997.
URI:http://hdl.handle.net/2142/86082
Other Identifier(s):(MiAaPQ)AAI9737029
Date Available in IDEALS:2015-09-28
Date Deposited:1997


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