Corn ear symmetry-based row number automatic detection method

Abstract

The invention discloses a rapid automatic detection method for counting a row number of corn ears. The method mainly comprises the three steps of firstly, building a row gap theoretical model according to characteristics that the row number of the ears is an even number and the cross sections of the ears have a rotation symmetry theory; secondly, collecting positive side images of the corn ears, displaying a kernel row gap in a highlighted manner, and obtaining an actual measurement mode of the row gap; and finally, performing matching check on the actual measurement mode and the theoretical model, thereby obtaining the theoretical model corresponding to a minimum distance between the actual measurement mode and the theoretical model, wherein the ear row number of the theoretical model isnamely the calculated ear row number. The method is simple in step, small in calculation amount and high in detection accuracy.

Description

technical field [0001] The invention relates to an automatic detection method for the number of rows based on the symmetry of corn ears. Background technique [0002] The number of grain rows on the corn ear (referred to as the number of ear rows) is one of the important agronomic traits of corn. The number of ear rows in different varieties of corn is quite different, and the growth conditions will also affect the number of ear rows. Accurately counting the number of ear rows is very important in scientific research such as corn breeding, cultivation and new variety DUS testing. [0003] At the earliest, the number of rows of corn ears required manual counting, which was prone to problems such as fatigue or carelessness, errors, and low efficiency. With the advancement of automation systems, automatic counting software and hardware systems have emerged. However, the current automatic counting methods either need to break off the complete fruit ear in advance, and then pro...

AI Technical Summary

Problems solved by technology

[0003] At the earliest, the number of rows of corn ears needs to be manually counted, which is prone to problems such as fatigue or carelessness and low efficiency

With the advancement of automation systems, automatic counting software and hardware systems have emerged. However, the current automatic counting methods either need to break off the complete fruit ear in advance, and then process the cross-sectional image, or require 360-degree image rotation method for image acquisition and image synthesis. Operation steps are troublesome

Moreover, the whole process calculates a large amount of data and the steps are cumbersome

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