A new method for lint percentage non-destructive  detection based on optical penetration imaging

Lijie Geng; Zhikun Ji; Ruiliang Zhang; Zhifeng Zhang; Yusheng Zhai; Wenyan Zhang

doi:10.9755/ejfa.2022.v34.i5.2854

Authors

Lijie Geng School of Physics and Electronic Engineering, and Henan Key Laboratory of Magnetoelectronic Information Functional Materials, Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, China
Zhikun Ji School of Physics and Electronic Engineering, and Henan Key Laboratory of Magnetoelectronic Information Functional Materials, Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, China
Ruiliang Zhang School of Physics and Electronic Engineering, and Henan Key Laboratory of Magnetoelectronic Information Functional Materials, Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, China
Zhifeng Zhang School of Physics and Electronic Engineering, and Henan Key Laboratory of Magnetoelectronic Information Functional Materials, Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, China
Yusheng Zhai School of Physics and Electronic Engineering, and Henan Key Laboratory of Magnetoelectronic Information Functional Materials, Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, China
Wenyan Zhang School of Physics and Electronic Engineering, and Henan Key Laboratory of Magnetoelectronic Information Functional Materials, Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, China

DOI:

https://doi.org/10.9755/ejfa.2022.v34.i5.2854

Abstract

Lint percentage of seed cotton is one of the important bases for pricing in the trading segment. Unfortunately, the conventional methods
of lint percentage are manually operated, which relies on the abundant experience of experts, and restrained by personal, physical and
environmental factors. Up to date, the calculation of the lint percentage of seed cotton has not fully automated. In this paper, we proposed
a non-destructive detection method for automatically obtaining lint percentage of seed cotton based on optical penetration imaging and
machine vision, for the first time to our knowledge. The cotton seed image was obtained by the penetration imaging setup with a LED
white backlight source. To accurately identify the number of cotton seeds, the image features of the cotton seed was studied and three
key features was been found, which are the circumference, area, and greyscale value, respectively. A calculation system based on the
three key features was presented to process the images and then automatically calculate the lint percentage of seed cotton. The first
step of the system is to segment the original image using adaptive thresholding followed by morphological operations. Afterwards, the
number of cotton seed was obtained by the three key features of the cotton seed. Then, the lint percentage was achieved by a professional
industry formula. The suggested lint percentage detection methods were verified by the experiments with two seed cotton varieties
samples of H219 and ZHM19. The experimental results indicated that the detection average accuracy of the developed system for seed
cotton varieties H219 and ZHM19 were 96.33% and 95.40%