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Sultan Qaboos University (2015)

Imaging Techniques For Detection Of Aspergillus Flavus Infection In Omani Dates

Mathew, Teena Ann.

Titre : Imaging Techniques For Detection Of Aspergillus Flavus Infection In Omani Dates

Auteur : Mathew, Teena Ann.

Université de soutenance : Sultan Qaboos University

Grade : Doctor of Philosophy in Soil and Water Management 2015

Résumé partiel
Detection of early stages of microbial infection on dates in processing and handling facilities by the present manual sorting method is not accurate. Fungal infection in date fruits causes possible mycotoxin production. Therefore, it is crucial to develop and implement a fast, non-destructive, objective, accurate and real-time technique to detect fungal infection in date handling facilities. The potential of three computer vision techniques such as Near infrared hyperspectral imaging (NIR-HSI), Near infrared (NIR) areascan imaging and RGB color imaging to detect A. flavus infection on dates in Oman was investigated. Several algorithms were developed and analyzed to detect and classify fungal infection. Also, the scope for methodology and implementation of the developed techniques on commercial aspect were investigated. Three popular date varieties (Fard, Khalas and Naghal) were used for this study. The samples were treated as three groups ; untreated control (UC), sterile control (SC-surface sterilized, washed and air-dried) and infected samples (IS-surface sterilized, washed, air-dried and fungus inoculated). Images were taken from infected samples on every alternate day during the incubation period of ten days. After pre-processing and image segmentation, significant features were extracted from the date images and applied to the statistical classifiers (linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA)). Also classification was carried out with only the top five most contributing features using stepwise linear discriminant analysis (SLDA) and stepwise quadratic discriminant analysis (SQDA). Two-class models (control vs. infected dates), six-class models (control, IS Day 2, IS Day 4, IS Day 6, IS Day 8 and IS Day 10) and pair-wise models (control vs. each stage of infection) were developed. Hyperspectral images of control and infested samples were acquired from 75 image slices at 10 nm intervals between 960 and 1700 nm. A total of 64 features were extracted from the top four most significant wavelengths (1120 nm, 1300 nm, 1610 nm and 1650 nm), having 10 histogram features and 6 statistical features at 4 wavelengths

Présentation (SHUAA)

Annonce (Almandumah)

Page publiée le 4 juin 2022