The Quality Analysis of Food and Vegetable from Image Processing

Abstract

The quality analysis of food and vegetable from image is hot topic now a day, where researchers make them better then pervious findings through different technique and methods. In this research we have review the literature, and find gape from them, and suggest better proposed approach, design the algorithm, developed a software to measure the quality from images, where accuracy of image show better results, and compare the results with Perouse work done so for.  The Application we use an open-source dataset and python language with tensor flow lite framework.  In this research we focus to sort food and vegetable from image, in the images, the application can sort and make them grading after process the images; it could create less errors them human base sorting errors by manual grading. Digital pictures datasets were created. The collected images arranged by classes. The classification accuracy of the system was about 94%. As fruits and vegetables play important role in our day to day life, the quality of fruits and vegetables is needed in evaluating agricultural produce, customer always buy good quality fruits and vegetables. This document is about quality detection of fruit and vegetables using images. Most of customers suffering due to unhealthy foods and vegetables by suppliers so there is no proper quality measurement level followed by hotel managements.  I have developed an application to measure the quality of the fruits and vegetables by using images, it will tell you how is your fruits and vegetables are fresh or rotten. A large number of algorithms have been used in this project, including digital images, ResNet, VGG16, CNN and Transfer Learning grading feature extraction. This application used an open source dataset of images and language used python, and designs a framework of system.