Abstract
Industrial Revolution 4.0 has made us people more professional, automating all production stages from office work to project work on farms. In the precision agriculture, it is very urgent to bring new and effective solutions to using artificial intelligence for people to use and improve the manual steps gradually, and increase the automation feature. So, automatic fruit recognition technique is the latest trend and effective technique in precision agriculture. This paper proposes a technical solution for fruit classification using deep learning. Automatic fruit identification using computer vision is considered a challenging task. This is because there are similarities between fruits and changes in the external environment such as light affect the fruit recognition model. Most previously implemented techniques have some limitations since their testing and evaluation is done using a limited set of data sets. Some implementations, does not consider changes to the external environment for the image are considered in this implementation. In this paper, exploring part of the deep learning algorithms was achieved and discovered strengths and weaknesses for these algorithms. The knowledge was gained on deep learning and a model was built that could recognize fruits from images.
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Acknowledgment
The authors wish to express their appreciation to the Ministry of Education and Training for supporting this research project as part of the Ministerial Program of Science and Technology CTB.2021.DNA. “Research on applying deep learning model to recognize ripe pineapple period in Quang Nam - Da Nang”.
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Nguyen, H.H.C., Luong, A.T., Trinh, T.H., Ho, P.H., Meesad, P., Nguyen, T.T. (2021). Intelligent Fruit Recognition System Using Deep Learning. In: Meesad, P., Sodsee, D.S., Jitsakul, W., Tangwannawit, S. (eds) Recent Advances in Information and Communication Technology 2021. IC2IT 2021. Lecture Notes in Networks and Systems, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-79757-7_2
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DOI: https://doi.org/10.1007/978-3-030-79757-7_2
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