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Local Fruit Classification and Recognition Using CNN

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dc.contributor.author Dey, Udoy Chandra
dc.contributor.author Pal, Rajesh Kumar
dc.contributor.author Turza, Toufiq Hasan
dc.date.accessioned 2022-02-13T03:49:48Z
dc.date.available 2022-02-13T03:49:48Z
dc.date.issued 2021-06-03
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/7076
dc.description.abstract Automatic fruits classification is becoming more popular day by day. Many of us have very few idea about local fruits so we can attest to them even if we don’t know about the local fruits. In our thesis, we will describes about a system that can automatically recognize local fruits using computer vision approach. It is very common process to identifying fruit however automatic fruit classification is not easy task depending on their object’s positions, shape, colors, etc. In our project, we have collected the samples from different local area and then we applied different deep learning models like Resnet-50, VGG-19, Inception-v3, MobileNet, etc. that used Convolutional Neural Network (CNN) techniques to detect local fruits and classify in different classes. Among them MobileNet, VGG-19 given us higher accuracy of 99% and 98%. We also proposed a best model based on our training accuracy. We have collected eight different types of local fruits to done the project. We have total 3240 samples among them for training purpose we used 60 percentage of image data, 20 percentage of image for validation and 20 percent of total image used for testing purpose. To get better result, we removed image background and then augmented them in various way. After training and testing we got satisfied result. As the result of this research model local fruits detection are classified, which can help in our daily life to identify local fruits en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Fruit classification en_US
dc.subject CNN en_US
dc.subject Automation system en_US
dc.title Local Fruit Classification and Recognition Using CNN en_US
dc.type Article en_US

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