X-ray detection of coal cane impurities sorting system: convenient and fast automatic identification of sugar cane impurities

 

Abstract:

The purpose of this study is to explore the application of X-ray detection technology in the sorting system of impurities in sugarcane and realize the automatic identification of impurities in sugarcane. Through the construction of X-ray detection system, combined with image processing and machine learning algorithm, the impurities inside sugarcane can be detected quickly and accurately. The experimental results show that the system can effectively identify impurities such as coal in sugarcane and improve the quality and efficiency of sugarcane processing.

Key words: X-ray detection; Sugarcane impurities; Automatic recognition; Image processing; Machine learning

 

I. Introduction

 

As an important sugar crop, the quality of sugarcane directly affects the development and economic benefits of sugar industry. However, in the process of sugar cane growth and processing, it is often difficult to avoid mixing various impurities, such as coal, stone, soil and so on. These impurities not only affect the quality of sugar cane, but also may cause damage to processing equipment and reduce production efficiency. Therefore, rapid and accurate identification and sorting of sugarcane impurities has become an important task in sugar production.

 

In recent years, with the continuous development of X-ray detection technology, its application in the field of substance detection and identification is more and more extensive. X-ray detection technology has the characteristics of non-contact and high penetration, and can realize the visual detection of the internal structure of substances. Therefore, the X-ray detection technology is applied in the sorting system of sugarcane impurities in order to realize the automatic identification of sugarcane impurities and improve the quality and efficiency of sugarcane processing.

2. Construction of sorting system for impurities of coal cane detected by X-ray

 

1. System composition

 

The impurity sorting system of coal cane is mainly composed of X-ray transmitter, X-ray receiver, image processor, controller and actuator. The X-ray transmitter sends out X-rays, which penetrate the sugarcane sample, are picked up by the X-ray receiver and converted into an image signal. The image processor processes the received image signal to extract the impurity information inside the sugarcane. According to the output result of the image processor, the controller controls the executive mechanism to sort the sugarcane containing impurities.

 

2. Image processing technology

 

Image processing technology is the core of the impurity sorting system of coal cane detected by X-ray. By preprocessing, segmentation and feature extraction of X-ray images, the internal impurities of sugarcane can be recognized automatically. Preprocessing includes image filtering and enhancement to improve image quality. The segmentation operation is to separate the sugarcane from the background and impurities. Feature extraction is to extract the shape, size, position and other feature information of impurities from the segmented image.

 

3. Machine learning algorithms

 

In order to improve the accuracy and efficiency of sugarcane impurity identification, machine learning algorithm is also introduced in this study. By training and learning a large number of sugarcane samples, the machine learning algorithm can automatically identify and classify impurities in sugarcane. In this study, deep learning algorithms such as convolutional neural network (CNN) were used to identify sugarcane impurities.

 

3. Experimental results and analysis

 

1. Experimental setup

 

A series of experiments were carried out to verify the effectiveness of the X-ray detection system for impurity sorting of coal cane. In the experiment, sugarcane samples of different varieties and different growth stages were used, and impurities such as coal, stone and soil were artificially added to them. During the experiment, we recorded the recognition accuracy and processing speed of the system.

 

2. Experimental results

 

The experimental results show that the X-ray detection system has high recognition accuracy and processing speed. Impurities such as coal and stone added to sugarcane samples can be accurately identified and sorted out by the system. At the same time, the processing speed of the system is also fast, which can meet the needs of actual production.

 

3. Result analysis

 

The non-contact and high penetration of X-ray detection technology enable the system to achieve rapid and accurate detection of impurities in sugarcane. The introduction of image processing technology and machine learning algorithm further improves the recognition accuracy and stability of the system. In addition, the system has a high degree of automation, which can reduce manual intervention and improve production efficiency.

 

Iv. Discussion and prospect

 

In this study, the X-ray detection technology was successfully applied to the sorting system of impurities in sugarcane, and the automatic identification of impurities in sugarcane was realized. However, in practical application, there are still some challenges and problems to be solved.

 

First of all, different varieties and different growth stages of sugarcane have differences in internal structure and density, which may affect the recognition accuracy of the system. Therefore, future research needs to further optimize image processing algorithms and machine learning models to adapt to the recognition needs of different sugarcane samples.

 

Secondly, the high cost of X-ray inspection equipment may limit its application in some small sugar companies. Future research can consider ways to reduce equipment costs and improve equipment performance to promote the application of this technology.

 

In addition, with the continuous development of artificial intelligence technology, more advanced algorithms and models can be considered to be introduced into the sugar cane impurity sorting system in the future to further improve the recognition accuracy and processing speed of the system.

 

V. Conclusion

 

In this study, the automatic identification of sugarcane impurities was realized by constructing a sorting system for impurity detection by X-ray. The experimental results show that the system has high recognition accuracy and processing speed, and can meet the needs of actual production. Future research can further optimize algorithms and models and reduce equipment costs to promote the popularization and application of this technology.