dc.contributor.author |
Appadoo, Anish |
|
dc.contributor.author |
Gopaul, Yashna |
|
dc.contributor.author |
Pudaruth, Sameerchand |
|
dc.date.accessioned |
2023-01-29T08:03:13Z |
|
dc.date.available |
2023-01-29T08:03:13Z |
|
dc.date.issued |
2023-01-29 |
|
dc.identifier.issn |
2210-142X |
EN |
dc.identifier.uri |
https://journal.uob.edu.bh:443/handle/123456789/4733 |
|
dc.description.abstract |
Nowadays, many people are unaware of the benefits of fruits and vegetables which has resulted in their reduced consumption.
This has inevitably led to a rise in diseases such as obesity, high blood pressure and heart diseases. To this end, we have developed
FruVegy which is an android app which can automatically identify fruits and vegetables and then display its nutritional values. The app
can identify forty different fruits/vegetables. The app is specially targeting school students who will find it easy and fun to use and this,
we believe, will increase their interest in the consumption of fruits and vegetables. Furthermore, the names of the fruits and vegetables
are also available in French and in Mauritian Creole. Our dataset consists of 1600 images from 40 different fruits and vegetables. There
was an equal number of images for each fruit/vegetable. To our knowledge, this is the largest dataset that currently exists in literature.
Features such as shape, colour and texture were extracted from each image. Different machine learning classifiers were tested but
random forest with 100 trees produced the best result with an accuracy of 90.6%. However, with TensorFlow, an average accuracy of
98.1% was obtained under different scenarios. In the future, we intend we increase our dataset and the number of features in order to
achieve an even higher accuracy. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
University of Bahrain |
en_US |
dc.subject |
fruits; vegetables; identification; mobile app; machine learning; computer vision. |
en_US |
dc.title |
FruVegy: An Android App for the Automatic Identification of Fruits and Vegetables using Computer Vision and Machine Learning |
en_US |
dc.type |
Article |
en_US |
dc.identifier.doi |
http://dx.doi.org/10.12785/ijcds/130114 |
|
dc.volume |
13 |
en_US |
dc.issue |
1 |
en_US |
dc.pagestart |
169 |
en_US |
dc.pageend |
178 |
en_US |
dc.contributor.authoraffiliation |
Department of Information and Communication Technologies, University of Mauritius, Reduit, Mauritius |
en_US |
dc.source.title |
International Journal of Computing and Digital Systems |
en_US |
dc.abbreviatedsourcetitle |
IJCDS |
en_US |