I. Introduction
Rice is one of the most important cereal grain crops. It
constitutes the world’s principle source of food, being the
basic grain for the planet’s largest population. For tropical
Asians it is the staple food and is the major source of dietary
energy and protein. In Southeast Asia alone, rice is the
staple food for 80% of the population[1].
In the current grain-handling systems, grain type and
quality are assessed by visual inspection. This evaluation
process is, however, tedious and time consuming. The
decision-making capabilities of a grain inspector can be
seriously affected by his/her physical condition such as
fatigue and eyesight, mental state caused by biases and work
pressure, and working condition such as improper lighting
condition, etc. Hence, this needs to the automation of
process by developing an imaging system that should
acquire the rice grain images, rectify, and analyze it.
The color of rice is one of the main factors of the
evaluating the quality. While detecting the rice varieties by
the color features, people adopt more RGB color space and
HSV color space; in addition, L*a*b* color space is also
commonly used to extract the color feature value [2,3].