From Outside to Inside: The Subtle Probing of Globular Fruits and Solanaceous Vegetables Using Machine Vision and Near-Infrared Methods
Machine vision and near-infrared light technology are widely used in fruits and vegetable grading, as an important means of agricultural non-destructive testing. The characteristics of fruits and vegetables can easily be automatically distinguished by these two technologies, such as appearance, shap...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/14/10/2395 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850205327445721088 |
|---|---|
| author | Junhua Lu Mei Zhang Yongsong Hu Wei Ma Zhiwei Tian Hongsen Liao Jiawei Chen Yuxin Yang |
| author_facet | Junhua Lu Mei Zhang Yongsong Hu Wei Ma Zhiwei Tian Hongsen Liao Jiawei Chen Yuxin Yang |
| author_sort | Junhua Lu |
| collection | DOAJ |
| description | Machine vision and near-infrared light technology are widely used in fruits and vegetable grading, as an important means of agricultural non-destructive testing. The characteristics of fruits and vegetables can easily be automatically distinguished by these two technologies, such as appearance, shape, color and texture. Nondestructive testing is reasonably used for image processing and pattern recognition, and can meet the identification and grading of single features and fusion features in production. Through the summary and analysis of the fruits and vegetable grading technology in the past five years, the results show that the accuracy of machine vision for fruits and vegetable size grading is 70–99.8%, the accuracy of external defect grading is 88–95%, and the accuracy of NIR and hyperspectral internal detection grading is 80.56–100%. Comprehensive research on multi-feature fusion technology in the future can provide comprehensive guidance for the construction of automatic integrated grading of fruits and vegetables, which is the main research direction of fruits and vegetable grading in the future. |
| format | Article |
| id | doaj-art-9edef37312d24c2ebbfb844e98f6bd6a |
| institution | OA Journals |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-9edef37312d24c2ebbfb844e98f6bd6a2025-08-20T02:11:08ZengMDPI AGAgronomy2073-43952024-10-011410239510.3390/agronomy14102395From Outside to Inside: The Subtle Probing of Globular Fruits and Solanaceous Vegetables Using Machine Vision and Near-Infrared MethodsJunhua Lu0Mei Zhang1Yongsong Hu2Wei Ma3Zhiwei Tian4Hongsen Liao5Jiawei Chen6Yuxin Yang7School of Mechanical Engineering, Xihua University, Chengdu 611743, ChinaSchool of Mechatronics, Chengdu Agricultural University, Chengdu 611130, ChinaInstitute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, ChinaInstitute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, ChinaInstitute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, ChinaSchool of Mechanical Engineering, Xihua University, Chengdu 611743, ChinaSchool of Mechanical Engineering, Xihua University, Chengdu 611743, ChinaSchool of Mechanical Engineering, Xihua University, Chengdu 611743, ChinaMachine vision and near-infrared light technology are widely used in fruits and vegetable grading, as an important means of agricultural non-destructive testing. The characteristics of fruits and vegetables can easily be automatically distinguished by these two technologies, such as appearance, shape, color and texture. Nondestructive testing is reasonably used for image processing and pattern recognition, and can meet the identification and grading of single features and fusion features in production. Through the summary and analysis of the fruits and vegetable grading technology in the past five years, the results show that the accuracy of machine vision for fruits and vegetable size grading is 70–99.8%, the accuracy of external defect grading is 88–95%, and the accuracy of NIR and hyperspectral internal detection grading is 80.56–100%. Comprehensive research on multi-feature fusion technology in the future can provide comprehensive guidance for the construction of automatic integrated grading of fruits and vegetables, which is the main research direction of fruits and vegetable grading in the future.https://www.mdpi.com/2073-4395/14/10/2395machine visionnear infrared technologyfruits and vegetablesgrading |
| spellingShingle | Junhua Lu Mei Zhang Yongsong Hu Wei Ma Zhiwei Tian Hongsen Liao Jiawei Chen Yuxin Yang From Outside to Inside: The Subtle Probing of Globular Fruits and Solanaceous Vegetables Using Machine Vision and Near-Infrared Methods Agronomy machine vision near infrared technology fruits and vegetables grading |
| title | From Outside to Inside: The Subtle Probing of Globular Fruits and Solanaceous Vegetables Using Machine Vision and Near-Infrared Methods |
| title_full | From Outside to Inside: The Subtle Probing of Globular Fruits and Solanaceous Vegetables Using Machine Vision and Near-Infrared Methods |
| title_fullStr | From Outside to Inside: The Subtle Probing of Globular Fruits and Solanaceous Vegetables Using Machine Vision and Near-Infrared Methods |
| title_full_unstemmed | From Outside to Inside: The Subtle Probing of Globular Fruits and Solanaceous Vegetables Using Machine Vision and Near-Infrared Methods |
| title_short | From Outside to Inside: The Subtle Probing of Globular Fruits and Solanaceous Vegetables Using Machine Vision and Near-Infrared Methods |
| title_sort | from outside to inside the subtle probing of globular fruits and solanaceous vegetables using machine vision and near infrared methods |
| topic | machine vision near infrared technology fruits and vegetables grading |
| url | https://www.mdpi.com/2073-4395/14/10/2395 |
| work_keys_str_mv | AT junhualu fromoutsidetoinsidethesubtleprobingofglobularfruitsandsolanaceousvegetablesusingmachinevisionandnearinfraredmethods AT meizhang fromoutsidetoinsidethesubtleprobingofglobularfruitsandsolanaceousvegetablesusingmachinevisionandnearinfraredmethods AT yongsonghu fromoutsidetoinsidethesubtleprobingofglobularfruitsandsolanaceousvegetablesusingmachinevisionandnearinfraredmethods AT weima fromoutsidetoinsidethesubtleprobingofglobularfruitsandsolanaceousvegetablesusingmachinevisionandnearinfraredmethods AT zhiweitian fromoutsidetoinsidethesubtleprobingofglobularfruitsandsolanaceousvegetablesusingmachinevisionandnearinfraredmethods AT hongsenliao fromoutsidetoinsidethesubtleprobingofglobularfruitsandsolanaceousvegetablesusingmachinevisionandnearinfraredmethods AT jiaweichen fromoutsidetoinsidethesubtleprobingofglobularfruitsandsolanaceousvegetablesusingmachinevisionandnearinfraredmethods AT yuxinyang fromoutsidetoinsidethesubtleprobingofglobularfruitsandsolanaceousvegetablesusingmachinevisionandnearinfraredmethods |