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FruitVegCNN: Power- and Memory-Efficient Classification of Fruits & Vegetables Using CNN in Mobile MPSoC

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posted on 23.07.2020 by Somdip Dey, Suman Saha, Amit Singh, Klaus D. Mcdonald-Maier

Fruit and vegetable classification using Convolutional Neural Networks (CNNs) has become a popular application in the agricultural industry, however, to the best of our knowledge no previously recorded study has designed and evaluated such an application on a mobile platform. In this paper, we propose a power-efficient CNN model, FruitVegCNN, to perform classification of fruits and vegetables in a mobile multi-processor system-on-a-chip (MPSoC). We also evaluated the efficacy of FruitVegCNN compared to popular state-of-the-art CNN models in real mobile plat- forms (Huawei P20 Lite and Samsung Galaxy Note 9) and experimental results show the efficacy and power efficiency of our proposed CNN architecture.

Funding

nosh/agri-tech-000001

National Centre for Nuclear Robotics (NCNR)

Engineering and Physical Sciences Research Council

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Robust remote sensing for multi-modal characterisation in nuclear and other extreme environments

Engineering and Physical Sciences Research Council

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History

Email Address of Submitting Author

somdip.dey@essex.ac.uk

ORCID of Submitting Author

0000-0001-6161-4637

Submitting Author's Institution

University of Essex

Submitting Author's Country

United Kingdom

Licence

Exports