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dc.contributor.authorHussain, Dostdar
dc.contributor.authorHussain, Israr
dc.contributor.authorIsmail, Muhammad
dc.contributor.authorAlabrah, Amerah
dc.contributor.authorSajid Ullah, Syed
dc.contributor.authorAlaghbari, Hayat Mansoor
dc.identifier.citationHussain, D., Hussain, I., Ismail, M., Alabrah, A., Sajid Ullah, S. & Alaghbari, H.M. (2022). A Simple and Efficient Deep Learning-Based Framework for Automatic Fruit Recognition. Computational Intelligence and Neuroscience, 1-8.en_US
dc.description.abstractAccurate detection and recognition of various kinds of fruits and vegetables by using the artificial intelligence (AI) approach always remain a challenging task due to similarity between various types of fruits and challenging environments such as lighting and background variations. Therefore, developing and exploring an expert system for automatic fruits’ recognition is getting more and more important after many successful approaches; however, this technology is still far from being mature. The deep learning-based models have emerged as state-of-the-art techniques for image segmentation and classification and have a lot of promise in challenging domains such as agriculture, where they can deal with the large variability in data better than classical computer vision methods. In this study, we proposed a deep learning-based framework to detect and recognize fruits and vegetables automatically with difficult real-world scenarios. The proposed method might be helpful for the fruit sellers to identify and differentiate various kinds of fruits and vegetables that have similarities. The proposed method has applied deep convolutional neural network (DCNN) to the undertakings of distinguishing natural fruit images of the Gilgit-Baltistan (GB) region as this area is famous for fruits’ production in Pakistan as well as in the world. The experimental outcomes demonstrate that the suggested deep learning algorithm has the effective capability of automatically recognizing the fruit with high accuracy of 96%. This high accuracy exhibits that the proposed approach can meet world application requirements.en_US
dc.publisherHindawi Limiteden_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleA Simple and Efficient Deep Learning-Based Framework for Automatic Fruit Recognitionen_US
dc.title.alternativeA Simple and Efficient Deep Learning-Based Framework for Automatic Fruit Recognitionen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© 2022 The Author(s)en_US
dc.subject.nsiVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550en_US
dc.source.journalComputational Intelligence and Neuroscienceen_US
dc.relation.projectKing Saud University, Riyadh, Saudi Arabia: RSP2022R476en_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal