Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image
Corn ear damage caused by peeling significantly influence the output and quality of corn harvest. Ear damage recognition is the basis to adjust working parameters and to reduce damage. Image processing is attracting increasing attentions in the field of agriculture. Conventional image processing met...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/10/3371 |
| _version_ | 1797568148729233408 |
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| author | Jun Fu Haikuo Yuan Rongqiang Zhao Zhi Chen Luquan Ren |
| author_facet | Jun Fu Haikuo Yuan Rongqiang Zhao Zhi Chen Luquan Ren |
| author_sort | Jun Fu |
| collection | DOAJ |
| description | Corn ear damage caused by peeling significantly influence the output and quality of corn harvest. Ear damage recognition is the basis to adjust working parameters and to reduce damage. Image processing is attracting increasing attentions in the field of agriculture. Conventional image processing methods are difficult to be used for recognizing corn ear damage caused by peeling in field harvesting. To address the this problem, in this paper, we propose a peeling damage recognition method based on RGB image. For our method, we develop a dictionary-learning-based method to recognize corn kernels and a thresholding method to recognize ear damage regions. To obtain better performance, we also develop the corroding algorithm and the expanding algorithm for the post-processing of recognized results. The experimental results demonstrate the practicality and accuracy of the proposed method. This study could provide the theoretical basis to develop online peeling damage detection system for corn ear harvesters. |
| first_indexed | 2024-03-10T19:52:22Z |
| format | Article |
| id | doaj.art-335eb010d4eb4de0834b9964aee0df43 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T19:52:22Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-335eb010d4eb4de0834b9964aee0df432023-11-20T00:16:23ZengMDPI AGApplied Sciences2076-34172020-05-011010337110.3390/app10103371Peeling Damage Recognition Method for Corn Ear Harvest Using RGB ImageJun Fu0Haikuo Yuan1Rongqiang Zhao2Zhi Chen3Luquan Ren4Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, ChinaKey Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, ChinaKey Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, ChinaCollege of Biological and Agricultural Engineering, Jilin University, Changchun 130022, ChinaKey Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, ChinaCorn ear damage caused by peeling significantly influence the output and quality of corn harvest. Ear damage recognition is the basis to adjust working parameters and to reduce damage. Image processing is attracting increasing attentions in the field of agriculture. Conventional image processing methods are difficult to be used for recognizing corn ear damage caused by peeling in field harvesting. To address the this problem, in this paper, we propose a peeling damage recognition method based on RGB image. For our method, we develop a dictionary-learning-based method to recognize corn kernels and a thresholding method to recognize ear damage regions. To obtain better performance, we also develop the corroding algorithm and the expanding algorithm for the post-processing of recognized results. The experimental results demonstrate the practicality and accuracy of the proposed method. This study could provide the theoretical basis to develop online peeling damage detection system for corn ear harvesters.https://www.mdpi.com/2076-3417/10/10/3371corn damagepeeling damagerecognition methodRGB imagecorn ear harvest |
| spellingShingle | Jun Fu Haikuo Yuan Rongqiang Zhao Zhi Chen Luquan Ren Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image Applied Sciences corn damage peeling damage recognition method RGB image corn ear harvest |
| title | Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image |
| title_full | Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image |
| title_fullStr | Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image |
| title_full_unstemmed | Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image |
| title_short | Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image |
| title_sort | peeling damage recognition method for corn ear harvest using rgb image |
| topic | corn damage peeling damage recognition method RGB image corn ear harvest |
| url | https://www.mdpi.com/2076-3417/10/10/3371 |
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