Maize Seed Test Method and System

Abstract

The invention discloses a corn seed test method and a system thereof. The method includes the steps of corn ear image processing and analysis, corn kernel image processing and analysis, and the like; the system includes a corn ear conveying device 110, a corn ear image acquisition device 120, The first PLC device 130 , the corn ear weighing device 140 , the first computer 150 , the corn ear barcode scanner 160 , the corn ear position detection device 170 and the like in the corn ear conveying device 110 conveying process. The invention has the advantages of simultaneously measuring multiple traits of multiple corn ears, greatly improving detection speed and the like.

Description

technical field [0001] The invention relates to the technical field of machine vision detection, in particular to a method for testing corn seeds and a system thereof. Background technique [0002] Corn is one of the three major crops in the world and an important food, feed and industrial raw material crop in the world. Ear traits and grain traits are important agronomic traits that determine the yield of maize. The acquisition of maize traits, that is, maize indoor testing, is an important link in the process of maize breeding to evaluate the quality of breeding materials and decide whether to choose. Ear length, ear diameter, ear length-to-width ratio, number of ear rows, number of grains in ear rows, ear shape, bald tip length, grain thickness, ear weight, and corn grain length, grain width, total grain number, total grain weight, The measurement of traits such as 100-kernel weight, while the current measurement of corn traits mainly uses manual measurement, the workloa...

AI Technical Summary

Problems solved by technology

Ear traits and grain traits are important agronomic traits that determine the yield of maize. The acquisition of maize traits, that is, maize indoor testing, is an important link in the process of maize breeding to evaluate the quality of breeding materials and decide whether to choose. Ear length, ear diameter, ear length-to-width ratio, number of ear rows, number of grains in ear rows, ear shape, bald tip length, grain thickness, ear weight, and corn grain length, grain width, total grain number, total grain weight, The measurement of traits such as 100-kernel weight, while the current measurement of corn traits mainly uses manual measurement, the workload of data collection is huge, and there are problems such as slow speed, low efficiency, and poor accuracy. Research on the automatic extraction technology of corn ear kernel traits will help reduce breeding Workers' workload, ensure the objectivity and accuracy of data, and improve work efficiency

[0003] At present, machine vision technology has been applied to the detection of important quality characteristics of agricultural products such as surface defects and damage, size and surface color. Some researchers have used machine vision to detect the property parameters of corn ears, placing a single corn ear on a rotating table to drive The ear of corn is rotated, and the sides of the ear are photographed with a camera and the images are stitched together. This method can detect all sides of the ear non-destructively, but the average detection time is 102s / ear. Too long detection time is not conducive to the measurement of large-scale ear traits; using scanning The speed of image collection is slow and the ears of corn are damaged, so it is not suitable for online detection; the detection of corn kernel traits currently mainly uses static image collection of kernels, but there is manual intervention

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