Image-Based Analysis of Fungal-Damaged Soybeans

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Agricultural Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Ahmad, Irfan Saleem

Contributors dc:contributor

• Reid, John F.

• Computer Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI9737029

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/86082