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Using this system, the seeds of grain and oil crops such as soybeans and mung beans were randomly detected. From the measured results, the maximum relative error was 0.2%. The error of the experimental results was far less than the national standard allowable error. Through the above counting method, adjusting the number of seeds can quickly and accurately adjust the number of seeds to 1000 or 500, and then the seed weight can be obtained through the balance of the seed weight.
For the application of seed counters, the system uses median filtering methods, improved inter-class maximum variance binarization method, Euclidean distance transformation, and regional seed point search algorithm to process and count seed images, and implements the above in hardware. Process. From the experimental results, it can be seen that the system has a good effect on the determination of the seed quantity, and can reach the national standard in terms of accuracy and speed, improve the accuracy and speed of 1000-grain seed measurement, and save human resources.
The 1000-grain weight, one of the most important indicators of seed vigor, has been measured manually in the past. This method is not only inefficient but also inaccurate. The seed counter can be used as much as possible during these tests. At present, computer vision inspection technology has begun to be applied to the detection of round objects. With the rapid development of image processing, computer vision, and pattern recognition technologies, domestic and foreign countries have also performed some useful methods for detecting circular objects based on computer vision technology. Exploration.