Banana ripeness classification using computer vision-based mobile application
The integration of smartphone applications with the increasingly growing influence of artificial intelligence provides users with new ways to do about anything and allows users to be practical. In this paper, a mobile application to identify the ripeness of banana fruits is built by implementing a...
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Main Authors: | , , , |
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Format: | Proceeding Paper |
Language: | English English |
Published: |
IEEE
2021
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Subjects: | |
Online Access: | http://irep.iium.edu.my/113445/7/113445_Banana%20ripeness%20classification%20using%20computer.pdf http://irep.iium.edu.my/113445/8/113445_Banana%20ripeness%20classification%20using%20computer_Scopus.pdf http://irep.iium.edu.my/113445/ https://ieeexplore.ieee.org/document/9467225 |
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Summary: | The integration of smartphone applications with
the increasingly growing influence of artificial intelligence
provides users with new ways to do about anything and allows users to be practical. In this paper, a mobile application to identify the ripeness of banana fruits is built by implementing a computer vision technique. Image classification is performed by adopting transfer learning to extract edges from a pretrained model. Convolutional neural network (CNN) model is used to train the classifier. Banana is chosen as an instance due to its short shelf life and widely consumed by Malaysian. For
this project, Google Colab is utilized for the code execution as
it is run on cloud and well-suited for machine learning.
TensorFlow Lite with Model Maker library simplified the
process of adapting and converting a TensorFlow neuralnetwork model to particular input data before deploying to an Android application. The result emerged with an accuracy of 98.25%. The app can instantly recognize banana live image, display the ripeness level on the screen based on highest percentage matched and display the ripeness, enabling the users to identify the banana ripeness quickly and easily. |
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