Rice Appearance Quality Detection Based On Computer Vision
rice quality detection system based on computer vision technology was developed. It uses grayscale transformation, automatic threshold segmentation, area marking and other methods to extract single rice grain images from the collected rice population images, and detect the cracks and chalky characteristics of single rice grains. Statistical and detection method studies were conducted. Ten characteristic parameters such as the area and circumference of rice grains were extracted as the detection features of whole milled rice, and principal component analysis was performed to determine the optimal threshold for distinguishing fully milled rice. The test results show that: the accuracy of cracked rice identification is 96.41%; the accuracy of chalky rice identification is 94.79%; the accuracy of whole milled rice identification is
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Original research was done by Wu Yanhong, Liu Muhua, Yang Jun, Zheng Huadong
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