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...

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Main Authors: Yuping Huang, Ziang Li, Zhouchen Bian, Haojun Jin, Guoqing Zheng, Dong Hu, Ye Sun, Chenlong Fan, Weijun Xie, Huimin Fang
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Foods
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Online Access:https://www.mdpi.com/2304-8158/14/2/286
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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.
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publishDate 2025-01-01
publisher MDPI AG
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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
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