In 2008 S.Sansomboonsuk et.al developed the appropriate algorithm of Image analysis for Rice Kernel Quality and a computer vision system was developed for evaluating the quality of rice kernels which used to extract features for touching kernels of Thai Jasmine rice (Pathumthani1). The touching kernel features consist of two forms of touching: point and line touching kernels. The shrinkage operation are used to separate touching features and Object recognitions are applied for the line touching feature. Fuzzy logic method was second-hand to sort out and classify the class of each kernel. The first one was still human inspection. They concluded correct results in evaluating the quality of rice kernels.[32].
B.K. Yadav et.al performed for milled whole kernels of ten Thai rice varieties ranging from low to high amylose content (16–29%) with three initial moisture levels (approximately, 8, 12 and 16% d.b.) for monitoring the