TELKOMNIKA Telecommunication, Computing, Electronics and Control
PhosopNet: An improved grain localization and classification by image augmentation

TELKOMNIKA Telecommunication, Computing, Electronics and Control
PhosopNet: An improved grain localization and classification by image augmentation

Feature transformation
Grain classification
Grain localization
Image augmentation
Transfer adaptation learning

Rice is a staple food for around 3.5 billion people in eastern, southern and
south-east Asia. Prior to being rice, the rice-grain (grain) is previously
husked and/or milled by the milling machine. Relevantly, the grain quality
depends on its pureness of particular grain specie (without the mixing
between different grain species). For the demand of grain purity inspection
by an image, many researchers have proposed the grain classification
(sometimes with localization) methods based on convolutional neural
network (CNN). However, those papers are necessary to have a large number
of labeling that was too expensive to be manually collected. In this paper, the
image augmentation (rotation, brightness adjustment and horizontal flipping)
is appiled to generate more number of grain images from the less data. From

the results, image augmentation improves the performance in CNN and bag-
of-words model. For the future moving forward, the grain recognition can be

easily done by less number of images.

Pakpoom Mookdarsanit, Lawankorn Mookdarsanit

http://journal.uad.ac.id/index.php/TELKOMNIKA

Oct 19, 2020

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