A comprehensive review of external quality measurements of fruits and vegetables using nondestructive sensing technologies
Fruits and vegetables have always had a significant economic impact on human survival, providing food security and boosting output with minimal input. This review focuses on an in-depth analysis of the grading criteria and the identification of exterior quality characteristics of major vegetables an...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Journal of Agriculture and Food Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666154324001054 |
| _version_ | 1797271294370119680 |
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| author | Tanjima Akter Tanima Bhattacharya Jung-Hyeon Kim Moon S. Kim Insuck Baek Diane E. Chan Byoung-Kwan Cho |
| author_facet | Tanjima Akter Tanima Bhattacharya Jung-Hyeon Kim Moon S. Kim Insuck Baek Diane E. Chan Byoung-Kwan Cho |
| author_sort | Tanjima Akter |
| collection | DOAJ |
| description | Fruits and vegetables have always had a significant economic impact on human survival, providing food security and boosting output with minimal input. This review focuses on an in-depth analysis of the grading criteria and the identification of exterior quality characteristics of major vegetables and fruits through various noninvasive spectroscopic and imaging methods, along with a brief discussion of their key components, schematic operations, potential for application in place of conventional approaches, and highlights the potential research gaps. In this review, the attention was focused on preprocessing, data analysis techniques, and the specific and overall values of performance accuracy by using a specific performance metric in relation to fruits and vegetables. Several machine learning (ML), as well as deep learning (DL) techniques, such as K-nearest neighbor (KNN), artificial neural networks (ANN), support vector machines (SVM), convolutional neural networks (CNN) with transfer learning (TL), generative adversarial networks (GAN) and recurrent neural network (RNN), have recently been used for inspection along with the processing of spectral data. ML and DL techniques have been proposed in recent publications for the external quality inspection of fruits and vegetables. |
| first_indexed | 2024-03-07T13:59:39Z |
| format | Article |
| id | doaj.art-f7d68bef02db4485ac87e03326c0da6a |
| institution | Directory Open Access Journal |
| issn | 2666-1543 |
| language | English |
| last_indexed | 2024-03-07T13:59:39Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Agriculture and Food Research |
| spelling | doaj.art-f7d68bef02db4485ac87e03326c0da6a2024-03-07T05:30:13ZengElsevierJournal of Agriculture and Food Research2666-15432024-03-0115101068A comprehensive review of external quality measurements of fruits and vegetables using nondestructive sensing technologiesTanjima Akter0Tanima Bhattacharya1Jung-Hyeon Kim2Moon S. Kim3Insuck Baek4Diane E. Chan5Byoung-Kwan Cho6Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-to, Yuseong-gu, Daejeon, 34134, Republic of KoreaDepartment of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-to, Yuseong-gu, Daejeon, 34134, Republic of Korea; Faculty of Applied Science, Lincoln University College, Petaling Jaya, 47301, Selangor Darul Ehsan, MalaysiaMajor of Smart Factory Convergence, Industry-Academic Cooperation Foundation, Kumoh National Institute of Technology, Gumi, 39177, Republic of KoreaEnvironmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States, Department of Agriculture, Beltsville, MD, 20705, USAEnvironmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States, Department of Agriculture, Beltsville, MD, 20705, USAEnvironmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States, Department of Agriculture, Beltsville, MD, 20705, USADepartment of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-to, Yuseong-gu, Daejeon, 34134, Republic of Korea; Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-to, Yuseong-gu, Daejeon, 34134, Republic of Korea; Corresponding author. Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-to, Yuseong-gu, Daejeon, 34134, Republic of Korea.Fruits and vegetables have always had a significant economic impact on human survival, providing food security and boosting output with minimal input. This review focuses on an in-depth analysis of the grading criteria and the identification of exterior quality characteristics of major vegetables and fruits through various noninvasive spectroscopic and imaging methods, along with a brief discussion of their key components, schematic operations, potential for application in place of conventional approaches, and highlights the potential research gaps. In this review, the attention was focused on preprocessing, data analysis techniques, and the specific and overall values of performance accuracy by using a specific performance metric in relation to fruits and vegetables. Several machine learning (ML), as well as deep learning (DL) techniques, such as K-nearest neighbor (KNN), artificial neural networks (ANN), support vector machines (SVM), convolutional neural networks (CNN) with transfer learning (TL), generative adversarial networks (GAN) and recurrent neural network (RNN), have recently been used for inspection along with the processing of spectral data. ML and DL techniques have been proposed in recent publications for the external quality inspection of fruits and vegetables.http://www.sciencedirect.com/science/article/pii/S2666154324001054External qualityNondestructive measurementSensing techniquesMachine learningDeep learningFruits and vegetables |
| spellingShingle | Tanjima Akter Tanima Bhattacharya Jung-Hyeon Kim Moon S. Kim Insuck Baek Diane E. Chan Byoung-Kwan Cho A comprehensive review of external quality measurements of fruits and vegetables using nondestructive sensing technologies Journal of Agriculture and Food Research External quality Nondestructive measurement Sensing techniques Machine learning Deep learning Fruits and vegetables |
| title | A comprehensive review of external quality measurements of fruits and vegetables using nondestructive sensing technologies |
| title_full | A comprehensive review of external quality measurements of fruits and vegetables using nondestructive sensing technologies |
| title_fullStr | A comprehensive review of external quality measurements of fruits and vegetables using nondestructive sensing technologies |
| title_full_unstemmed | A comprehensive review of external quality measurements of fruits and vegetables using nondestructive sensing technologies |
| title_short | A comprehensive review of external quality measurements of fruits and vegetables using nondestructive sensing technologies |
| title_sort | comprehensive review of external quality measurements of fruits and vegetables using nondestructive sensing technologies |
| topic | External quality Nondestructive measurement Sensing techniques Machine learning Deep learning Fruits and vegetables |
| url | http://www.sciencedirect.com/science/article/pii/S2666154324001054 |
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