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Fruit Classification and Defect Detection System Using Faster Region Convolutional Neural Network

Aziz, Amir Aizat (2019) Fruit Classification and Defect Detection System Using Faster Region Convolutional Neural Network. IRC, Universiti Teknologi PETRONAS. (Submitted)

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Malaysia is still a net importer of both fresh and refined fruits and the fresh fruit export price is around USD 174 million. Various methods are presented to improve fruit and vegetable production. Using the latest technologies and knowledge-based production systems, conventional farms will be turned into sustainable farms. Since consumers use the appearance of fruits to first evaluate the quality of fresh food, the presence of skin defects appears to be one of the most influential factors in fresh food quality and price. For this purpose, packing houses need suitable systems capable of detecting skin deficiencies in fruits. The problem statement of this study is packing houses do not have a proper method that can identify the deficiencies in fruits using computer vision. It also involved computer vision technology approach and machine learning doing supervised learning that used Faster R-CNN model as element to achieve the objective of the project. Hence, this study been conducted to develop a method to classify the types of fruits and identify defect of the fruits based on their outer skin.

Item Type: Final Year Project
Academic Subject : Academic Department - Information Communication Technology
Subject: Q Science > Q Science (General)
Divisions: Sciences and Information Technology > Computer and Information Sciences
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 09 Sep 2021 19:57
Last Modified: 09 Sep 2021 19:57
URI: http://utpedia.utp.edu.my/id/eprint/20890

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