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https://hdl.handle.net/2142/86082
Description
Title
Image-Based Analysis of Fungal-Damaged Soybeans
Author(s)
Ahmad, Irfan Saleem
Issue Date
1997
Doctoral Committee Chair(s)
Reid, John F.
Department of Study
Agricultural Engineering
Discipline
Agricultural Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
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.
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