Dissertation
Dissertation > Agricultural Sciences > Crop > Cereal crops > Rice

Studies on Rapid Measurement System of Rice Exterior Quality and Its Application

Author ChenDingShan
Tutor XiaoLangTao
School Hunan Agricultural University
Course Biophysics
Keywords Rice character Visual C++ Image processing Area of disease spot Rapid detection
CLC S511
Type Master's thesis
Year 2011
Downloads 31
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Rice exterior qualities included the ratio of chalky grain, chalkiness area, chalkiness degree, length, width and length/width of grain. For long time, the detection of grain appearance quality has been still rested on naked-eye observation which not only lacksed objectiveness and repetitiveness but also caused strong subjectiveness and poor consistency. To solve the problem, the Visual C++.NET was employed as a tool to develop a computer image processing system ChalkinessV2.0 for detection of chalkiness characteristics of rice which included the ratio of chalky grain, chalkiness degree, shape of grain, and other in the national standard of ministry of agriculture.Main results of the experiments are as follows:(1) The system was composed of modules of image preprocessing, sample identification, chalkiness distinguishing, ratio of length and width detection, statistics and analysis and results export. The system had outstanding traits such as friendly interface, powerful function and easy operation, and could export all the related indexes automatically with only once import of the parameter. Compared with the traditional method used at present, the new system was objective, rapid as well as repeatable.(2) The system could also be employed to detect RLA, leaf area of rice and arabidopsis based on its powerful ability to distinguish the border between disease spot and the marge of healthy leaf. The new system significantly reduced the error produced by naked-eye recognition and overcomed the deficiency of computer which could not carry fuzzy recognition. As a result, outputs with objective, rapid and good repetition could be achieved, so the new system could be widely applied to detect the area of leaf and disease spot such as soybean gray mold, tobacco brown spot, northern leaf blight and southern leaf blight of corn and peanut brown spot.

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