<span>Presently, the demands for rice are increasing. This will affects the need for producing and sorting rice grain in faster and exceed the normal requirement. However, the manual rice classification using naked eyes are not very accurate and only professionals are able to do it. Machine learning is found to be a suitable technique for rice classification in producing an accurate result and faster solution. Thus, a study on the classification of rice grain using an image processing technique is presented. The rice grain image went through the pre-processing process which includes the grayscale and binary conversion, and segmentation before the feature extraction process. Four attributes of shape descriptor which are area, perimeter, major axis length, and minor axis length and three attributes of color descriptor which are hue, saturation and value were extracted from each rice grain image. In another note, a Multi-class Support Vector Machine (SVM) is used to classify the three types of rice grain which are basmathi, ponni and brown rice. The performance of the proposed study is evaluated to 90 testing images which returned 92.22% of classification accuracy. The study is expected to assist the Agrotechnology industry in automatic classification of rice grain in the future.</span>
A viral infection which is named as Coronavirus disease 2019 (COVID-19) is triggered by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) [1]. Presently, there are reportedly almost two million infections and more than 100,000 worldwide deaths from the disease caused by this virus [2]. The medical and research groups are working together to reduce the serious effects of the outbreak. With more than 327 000 cases registered, the United States of America reported the highest rise since the beginning of August 2020, in newly reported cases. Meanwhile, the progressive decline in the occurrence of cases and deaths in the area of South East Asia were reported [3].
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