Computer Vision Based Local Fruit Recognition
Md. Robel Mia1, Md. Jueal Mia2, Anup Majumder3, Soummo Supriya4, Md. Tarek Habib5
1Md. Robel Mia, Department of CSE, Daffodil International University, Dhaka, Bangladesh.
2Md. Jueal Mia, Department of CSE, Daffodil International University, Dhaka, Bangladesh.
3Anup Majumder, Department of CSE, Jahangirnagar University, Dhaka, Bangladesh.
4Soummo Supriya, Department of CSE, Daffodil International University, Dhaka, Bangladesh.
5Md. Tarek Habib, Department of CSE, Jahangirnagar University, Daffodil International University, Bangladesh.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2810-2820 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9789109119/2019©BEIESP | DOI: 10.35940/ijeat.A9789.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Computer vision, Feature extraction, Image segmentation, Local fruit, Performance metrics, Support vector machine (SVM). I