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Tạp chí khoa học ĐHCT
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Bài báo - Tạp chí
(2019) Trang: 92-96
Tạp chí: The International Conference on Advanced COMPuting and Applications (ACOMP)

Rice is one of the profit export products of Vietnam but how to detect quality of the rice is still difficult. This work proposes an approach for rice quality classification. In this approach, image processing algorithms and machine learning methods were used to recognize and classify two difference categories of rice (whole rice and broken rice) based on the rice’s size of the national standard of rice quality evaluation, using Convolutional Neural Network (CNN). Experimental results for 2000 real images give 93.85% accuracy. The system also used Support Vector Machines method with HOG features and k-Nearest Neighbors methods in order to classify and compare the accuracy of those algorithms which show the results of 85.06% and 84.30% accuracy, respectively. These results show that rice quality evaluation and classification could be automatically done using Deep Learning approach. 

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