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Apple adulteration detection and quality sorting

dc.contributor.advisorRasheduzzaman, Mirza
dc.contributor.advisorShawon, Md. Mehedi Hasan
dc.contributor.authorBiswas, Prithwiraj
dc.contributor.authorIsty, Rubaiyeat Tanzir
dc.contributor.authorRimon, Rafsun Ahamed
dc.contributor.authorZaman, Zarif Ishmam
dc.contributor.departmentDepartment of Electrical and Electronic Engineering
dc.date.accessioned2025-07-03T03:49:15Z
dc.date.available2025-07-03T03:49:15Z
dc.date.copyright2025
dc.date.issued2025
dc.descriptionCataloged from PDF version of final year design project.
dc.descriptionIncludes bibliographical references (pages 91-92).
dc.descriptionThis final year design project is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2025.en_US
dc.description.abstractIn the pyramid of human necessities, food reigns high. Therefore, reliable adulteration detection and quality identification of any type of food not only keeps the fraudulent business practitioners in check but also ensures good quality food is being produced and consumed for one’s hard earned capital. Unlike disaster detection kits such as gas leakage or fire detection - here accuracy needs to be prioritized over speed as one’s long term health is concerned. In this project, we narrowed down our focus to just apples as it is the most consumed fruit in the world and we will be making our own dataset for improved accuracy and robustness. We have developed a medium sized portable box that can detect apple adulteration and quality - which, thanks to our inhouse dataset, can be expanded upon for industrial use.en_US
dc.description.degreeB.Sc. in Electrical and Electronic Engineering
dc.description.statementofresponsibilityPrithwiraj Biswas
dc.description.statementofresponsibilityRubaiyeat Tanzir Isty
dc.description.statementofresponsibilityRafsun Ahamed Rimon
dc.description.statementofresponsibilityZarif Ishmam Zaman
dc.format.extent110 pages
dc.identifier.otherID 21321044
dc.identifier.otherID 21321043
dc.identifier.otherID 21121035
dc.identifier.otherID 20221033
dc.identifier.urihttp://hdl.handle.net/10361/26440
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University project reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectAdulteration detectionen_US
dc.subjectQuality identificationen_US
dc.subjectImage processingen_US
dc.subjectMachine learningen_US
dc.subjectConvolutional neural networken_US
dc.subjectRaspberry Pien_US
dc.subjectReal-Time detectionen_US
dc.subject.lcshMachine learning.
dc.subject.lcshImage processing.
dc.subject.lcshNeural networks (Computer science).
dc.titleApple adulteration detection and quality sortingen_US
dc.typeProject Reporten_US

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