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