Automatic detection method of row number based on corn ear symmetry

Abstract

The rapid automatic detection method for counting the number of rows of corn ears of the present invention mainly has three major steps. First, according to the theory that the number of rows of ears is an even number and the theory of rotational symmetry of the cross-section surface of the ears, a theoretical model of the row gap is established. Secondly, collect the front and side images of corn ears, highlight the grain row gap, and obtain the actual measurement mode of the row gap. Finally, the measured model is matched with the theoretical model to obtain the theoretical model corresponding to the one with the smallest distance between the two. The number of ear rows in the theoretical model is the desired number of ear rows. The invention has simple steps, small amount of calculation and high 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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