Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of Tomatoes
Tomato, as the vegetable queen, is cultivated worldwide due to its rich nutrient content and unique flavor. Nondestructive technology provides efficient and noninvasive solutions for the quality assessment of tomatoes. However, processing the substantial datasets to achieve a robust model and enhanc...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Foods |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-8158/14/2/286 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588525261815808 |
|---|---|
| author | Yuping Huang Ziang Li Zhouchen Bian Haojun Jin Guoqing Zheng Dong Hu Ye Sun Chenlong Fan Weijun Xie Huimin Fang |
| author_facet | Yuping Huang Ziang Li Zhouchen Bian Haojun Jin Guoqing Zheng Dong Hu Ye Sun Chenlong Fan Weijun Xie Huimin Fang |
| author_sort | Yuping Huang |
| collection | DOAJ |
| description | Tomato, as the vegetable queen, is cultivated worldwide due to its rich nutrient content and unique flavor. Nondestructive technology provides efficient and noninvasive solutions for the quality assessment of tomatoes. However, processing the substantial datasets to achieve a robust model and enhance detection performance for nondestructive technology is a great challenge until deep learning is developed. The aim of this paper is to provide a systematical overview of the principles and application for three categories of nondestructive detection techniques based on mechanical characterization, electromagnetic characterization, as well as electrochemical sensors. Tomato quality assessment is analyzed, and the characteristics of different nondestructive techniques are compared. Various data analysis methods based on deep learning are explored and the applications in tomato assessment using nondestructive techniques with deep learning are also summarized. Limitations and future expectations for the quality assessment of the tomato industry by nondestructive techniques along with deep learning are discussed. The ongoing advancements in optical equipment and deep learning methods lead to a promising outlook for the application in the tomato industry and agricultural engineering. |
| format | Article |
| id | doaj-art-f7aa4427e6e24d2591030ebb1ef12cbd |
| institution | Kabale University |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-f7aa4427e6e24d2591030ebb1ef12cbd2025-01-24T13:33:08ZengMDPI AGFoods2304-81582025-01-0114228610.3390/foods14020286Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of TomatoesYuping Huang0Ziang Li1Zhouchen Bian2Haojun Jin3Guoqing Zheng4Dong Hu5Ye Sun6Chenlong Fan7Weijun Xie8Huimin Fang9College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Flexible Electronics (Future Technologies) and Institute of Advanced Materials (IAM), Nanjing Tech University, Nanjing 211816, ChinaCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, ChinaCollege of Food Science and Light Industry, Nanjing Tech University, Nanjing 211816, ChinaCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaTomato, as the vegetable queen, is cultivated worldwide due to its rich nutrient content and unique flavor. Nondestructive technology provides efficient and noninvasive solutions for the quality assessment of tomatoes. However, processing the substantial datasets to achieve a robust model and enhance detection performance for nondestructive technology is a great challenge until deep learning is developed. The aim of this paper is to provide a systematical overview of the principles and application for three categories of nondestructive detection techniques based on mechanical characterization, electromagnetic characterization, as well as electrochemical sensors. Tomato quality assessment is analyzed, and the characteristics of different nondestructive techniques are compared. Various data analysis methods based on deep learning are explored and the applications in tomato assessment using nondestructive techniques with deep learning are also summarized. Limitations and future expectations for the quality assessment of the tomato industry by nondestructive techniques along with deep learning are discussed. The ongoing advancements in optical equipment and deep learning methods lead to a promising outlook for the application in the tomato industry and agricultural engineering.https://www.mdpi.com/2304-8158/14/2/286tomato qualitynondestructive detection techniquesdeep learningapplications |
| spellingShingle | Yuping Huang Ziang Li Zhouchen Bian Haojun Jin Guoqing Zheng Dong Hu Ye Sun Chenlong Fan Weijun Xie Huimin Fang Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of Tomatoes Foods tomato quality nondestructive detection techniques deep learning applications |
| title | Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of Tomatoes |
| title_full | Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of Tomatoes |
| title_fullStr | Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of Tomatoes |
| title_full_unstemmed | Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of Tomatoes |
| title_short | Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of Tomatoes |
| title_sort | overview of deep learning and nondestructive detection technology for quality assessment of tomatoes |
| topic | tomato quality nondestructive detection techniques deep learning applications |
| url | https://www.mdpi.com/2304-8158/14/2/286 |
| work_keys_str_mv | AT yupinghuang overviewofdeeplearningandnondestructivedetectiontechnologyforqualityassessmentoftomatoes AT ziangli overviewofdeeplearningandnondestructivedetectiontechnologyforqualityassessmentoftomatoes AT zhouchenbian overviewofdeeplearningandnondestructivedetectiontechnologyforqualityassessmentoftomatoes AT haojunjin overviewofdeeplearningandnondestructivedetectiontechnologyforqualityassessmentoftomatoes AT guoqingzheng overviewofdeeplearningandnondestructivedetectiontechnologyforqualityassessmentoftomatoes AT donghu overviewofdeeplearningandnondestructivedetectiontechnologyforqualityassessmentoftomatoes AT yesun overviewofdeeplearningandnondestructivedetectiontechnologyforqualityassessmentoftomatoes AT chenlongfan overviewofdeeplearningandnondestructivedetectiontechnologyforqualityassessmentoftomatoes AT weijunxie overviewofdeeplearningandnondestructivedetectiontechnologyforqualityassessmentoftomatoes AT huiminfang overviewofdeeplearningandnondestructivedetectiontechnologyforqualityassessmentoftomatoes |