Özet:
The quality analysis of vegetable and food 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
uses 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 sorts 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 main role in
day-to-day life, the quality of fruits and vegetables is necessary 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
software to determine the quality of the vegetables and fruits by using images, it will
tell you how your fruits and vegetables are fresh or rotten. Some algorithms reviewed
in this thesis, 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.
