Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/134224
Title: Automated External Skin Defect Detection System for Mangoes using Computational Intelligence and Machine Learning Techniques
Researcher: Deepa P
Guide(s): Dr.S.N.Geethalakshmi
Keywords: Adaptive Fuzzy Switching
Fuzzy Possibilistic C-means
Support Vector Machine
University: Avinashilingam Deemed University For Women
Completed Date: 4.8.2016
Abstract: Detection of defects in mango fruit is a challenging problem in automatic inspection process newlineand is heavily used by the food industries, particularly by the import/export companies worldwide. It is newlinean important task of quality control which helps to reduce rejection of fruits and time spent on manual newlineinspection. Fruit quality analysis is often based on its appearance using characteristics of skin or newlinesurface. Based on this fact, this research work is designed to identify skin or surface defects in mango fruit. The main objective of the research work thus is to design an automated external skin defect detection system for mango fruit using image processing and machine learning techniques in an accurate and time efficient manner. Major objectives: newlineTo improve the quality of the mango images using denoising methods to reduce the impulse noise for further processing. newlineTo perform clustering based segmentation on mango images to segment the defected regions. newlineTo perform feature extraction techniques to identify and extract characteristics that represents a mango image for quality classification. newlineTo design and develop classification model that uses the features extracted to detect the defects.
Pagination: 213 p.
URI: http://hdl.handle.net/10603/134224
Appears in Departments:Department of Computer Science

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pdeepa_intro.pdf510.41 kBAdobe PDFView/Open


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