Rasheduzzaman, MirzaShawon. Md. Mehedi HasanBiswas, PrithwirajIsty, Rubaiyeat TanzirRimon, Rafsun AhamedZaman, Zarif Ishmam2025-07-142025-07-1420252025ID 21321044ID 21321043ID 21121035ID 20221033http://hdl.handle.net/10361/26472Cataloged from PDF version of final year design project.Includes bibliographical references (pages 91-92).This 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.In 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.110 pagesenBRAC 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.Adulteration detectionQuality identificationImage processingMachine learningConvolutional neural networkRaspberry PiReal-Time detectionMachine learning.Image processing--Technological innovations.Real-Time--Data processing.Project Report