The Detection of Impurity Content in Machine-Picked Seed Cotton Based on Image Processing and Improved YOLO V4
The detection of cotton impurity rates can reflect the cleaning effect of cotton impurity removal equipment, which plays a vital role in improving cotton quality and economic benefits. Therefore, several studies are being carried out to improve detection accuracy. Image processing technology is incr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/12/1/66 |
| _version_ | 1797496708105502720 |
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| author | Chengliang Zhang Tianhui Li Wenbin Zhang |
| author_facet | Chengliang Zhang Tianhui Li Wenbin Zhang |
| author_sort | Chengliang Zhang |
| collection | DOAJ |
| description | The detection of cotton impurity rates can reflect the cleaning effect of cotton impurity removal equipment, which plays a vital role in improving cotton quality and economic benefits. Therefore, several studies are being carried out to improve detection accuracy. Image processing technology is increasingly used in cotton impurity detection, in which deep learning technology based on convolution neural networks has shown excellent results in image classification, segmentation, target detection, etc. However, most of these applications focus on detecting foreign fibers in lint, which is of little significance to the parameter adjustment of cotton impurity removal equipment. For this reason, our goal was to develop an impurity detection system for seed cotton. In image segmentation, we propose a multi-channel fusion segmentation algorithm to segment the machine-picked seed cotton image. We collected 1017 images of machine-picked seed cotton as a dataset to train the detection model and tested and recognized 100 groups of samples, with an average recognition rate of 94.1%. Finally, the image segmented by the multi-channel fusion algorithm is input into the improved YOLOv4 network model for classification and recognition, and the established V–W model calculates the content of all kinds of impurities. The experimental results show that the impurity content in machine-picked cotton can be obtained effectively, and the detection accuracy of the impurity rate can increase by 5.6%. |
| first_indexed | 2024-03-10T03:07:26Z |
| format | Article |
| id | doaj.art-4c915acbd81c46698b474009fdbdda9a |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T03:07:26Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-4c915acbd81c46698b474009fdbdda9a2023-11-23T12:37:42ZengMDPI AGAgronomy2073-43952021-12-011216610.3390/agronomy12010066The Detection of Impurity Content in Machine-Picked Seed Cotton Based on Image Processing and Improved YOLO V4Chengliang Zhang0Tianhui Li1Wenbin Zhang2School of Mechanical Engineering, University of Jinan, Jinan 250022, ChinaSchool of Mechanical Engineering, University of Jinan, Jinan 250022, ChinaSchool of Mechanical Engineering, University of Jinan, Jinan 250022, ChinaThe detection of cotton impurity rates can reflect the cleaning effect of cotton impurity removal equipment, which plays a vital role in improving cotton quality and economic benefits. Therefore, several studies are being carried out to improve detection accuracy. Image processing technology is increasingly used in cotton impurity detection, in which deep learning technology based on convolution neural networks has shown excellent results in image classification, segmentation, target detection, etc. However, most of these applications focus on detecting foreign fibers in lint, which is of little significance to the parameter adjustment of cotton impurity removal equipment. For this reason, our goal was to develop an impurity detection system for seed cotton. In image segmentation, we propose a multi-channel fusion segmentation algorithm to segment the machine-picked seed cotton image. We collected 1017 images of machine-picked seed cotton as a dataset to train the detection model and tested and recognized 100 groups of samples, with an average recognition rate of 94.1%. Finally, the image segmented by the multi-channel fusion algorithm is input into the improved YOLOv4 network model for classification and recognition, and the established V–W model calculates the content of all kinds of impurities. The experimental results show that the impurity content in machine-picked cotton can be obtained effectively, and the detection accuracy of the impurity rate can increase by 5.6%.https://www.mdpi.com/2073-4395/12/1/66machine adoptseed cottonneural networkimpurity identificationimpurity rateimage segmentation |
| spellingShingle | Chengliang Zhang Tianhui Li Wenbin Zhang The Detection of Impurity Content in Machine-Picked Seed Cotton Based on Image Processing and Improved YOLO V4 Agronomy machine adopt seed cotton neural network impurity identification impurity rate image segmentation |
| title | The Detection of Impurity Content in Machine-Picked Seed Cotton Based on Image Processing and Improved YOLO V4 |
| title_full | The Detection of Impurity Content in Machine-Picked Seed Cotton Based on Image Processing and Improved YOLO V4 |
| title_fullStr | The Detection of Impurity Content in Machine-Picked Seed Cotton Based on Image Processing and Improved YOLO V4 |
| title_full_unstemmed | The Detection of Impurity Content in Machine-Picked Seed Cotton Based on Image Processing and Improved YOLO V4 |
| title_short | The Detection of Impurity Content in Machine-Picked Seed Cotton Based on Image Processing and Improved YOLO V4 |
| title_sort | detection of impurity content in machine picked seed cotton based on image processing and improved yolo v4 |
| topic | machine adopt seed cotton neural network impurity identification impurity rate image segmentation |
| url | https://www.mdpi.com/2073-4395/12/1/66 |
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