谷物外观品质检测方法的研究
[Abstract]:The yield and quality of grain have great influence on people's life. At present, most of the grain detection experiments remain at the stage of artificial naked eye observation. Applying image processing to grain appearance classification can greatly improve the efficiency of grain screening. Compared with naked eye observation, the use of computer to detect grain appearance automatically has the advantages of high speed, high efficiency and good reusability. It is very worthy of research and development in the field of agricultural product classification. It is of high theoretical value and practical significance to analyze the appearance parameters of grain by processing the grain image and to evaluate the grain classification. Some characteristic parameters, such as the length, width and pixels occupied in the image, can be obtained from the image, and these parameters can be used to judge the grain shape and integrity. The grain appearance analysis algorithm studied in this paper is universal and can be applied to rice, mung bean, brown rice and oats. The main contents and innovations of this paper are as follows: (1) A complete system is designed, which includes the grain appearance collection system and the grain image analysis system. This system can be used instead of human observation to achieve the purpose of grain classification. (2) in the detection of a batch of cereals, there may be superposition and coincidence of grain, resulting in errors in grain parameter statistics. The watershed segmentation algorithm can be used to deal with this situation effectively. The algorithm can cut the grain edge which has not complex adhesion to achieve grain segmentation. However, the algorithm also has some shortcomings, such as excessive segmentation, often dividing a grain region into two blocks. By improving the algorithm in this paper, this situation can be avoided effectively.
【学位授予单位】:北方工业大学
【学位级别】:硕士
【学位授予年份】:2017
【分类号】:S126;TP391.41
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