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Computer Vision Based Local Fruit Recognition

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dc.contributor.author Mia, Md. Robel
dc.contributor.author Mia, Md. Jueal
dc.contributor.author Majumder, Anup
dc.contributor.author Supriya, Soummo
dc.contributor.author Habib, Md. Tarek
dc.date.accessioned 2022-01-26T10:09:55Z
dc.date.available 2022-01-26T10:09:55Z
dc.date.issued 2019-10
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6895
dc.description.abstract Abstract: Bangladesh is an agricultural country having a tropical monsoon climate. A large variety of tropical and sub-tropical fruits abound in Bangladesh. People of Bangladesh are fruit-lovers too. Currently, most of the people of this country are failing to recognize many of the rare local fruits and the number of this portion of people is increasing day by day. Thus, not only the natural heritage but also good sources of food are being diminished. Performing a machine vision based recognition of these fruits can help people recognize them. In this paper, we perform an in-depth exploration of a computer vision approach for recognizing rare local fruits of Bangladesh. A number of rare local fruits are classified based on the features extracted from their images. For our experiment, we have used a total of 480 images of 6 rare local fruits. We perform some preprocessing on the captured image and then expected features are extracted using image segmentation. Classification of the fruits is accomplished using support vector machines (SVMs). We have achieved 94.79% classification accuracy, which is not only good but also promising for future research. en_US
dc.language.iso en_US en_US
dc.publisher International Journal of Engineering and Advanced Technology en_US
dc.subject Computer vision en_US
dc.subject Feature extraction en_US
dc.subject Image segmentation en_US
dc.subject Local fruit en_US
dc.subject Performance metrics en_US
dc.subject Support vector machine (SVM) en_US
dc.title Computer Vision Based Local Fruit Recognition en_US
dc.type Article en_US


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