guide.pdf (2.9 MB)
Download file

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

Download (2.9 MB)
posted on 2020-07-23, 14:24 authored by Somdip DeySomdip 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.



National Centre for Nuclear Robotics (NCNR)

Engineering and Physical Sciences Research Council

Find out more...

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

Engineering and Physical Sciences Research Council

Find out more...


Email Address of Submitting Author

ORCID of Submitting Author


Submitting Author's Institution

University of Essex

Submitting Author's Country

  • United Kingdom