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Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation
Cherry tomato (Solanum lycopersicum) is popular with consumers over the world due to its special flavor. Soluble solids content (SSC) and firmness are two key metrics for evaluating the product qualities. In this work, we develop non-destructive testing techniques for SSC and fruit firmness based on...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9108868/ https://www.ncbi.nlm.nih.gov/pubmed/35586212 http://dx.doi.org/10.3389/fpls.2022.860656 |
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author | Xiang, Yun Chen, Qijun Su, Zhongjing Zhang, Lu Chen, Zuohui Zhou, Guozhi Yao, Zhuping Xuan, Qi Cheng, Yuan |
author_facet | Xiang, Yun Chen, Qijun Su, Zhongjing Zhang, Lu Chen, Zuohui Zhou, Guozhi Yao, Zhuping Xuan, Qi Cheng, Yuan |
author_sort | Xiang, Yun |
collection | PubMed |
description | Cherry tomato (Solanum lycopersicum) is popular with consumers over the world due to its special flavor. Soluble solids content (SSC) and firmness are two key metrics for evaluating the product qualities. In this work, we develop non-destructive testing techniques for SSC and fruit firmness based on hyperspectral images and the corresponding deep learning regression model. Hyperspectral reflectance images of over 200 tomato fruits are derived with the spectrum ranging from 400 to 1,000 nm. The acquired hyperspectral images are corrected and the spectral information are extracted. A novel one-dimensional (1D) convolutional ResNet (Con1dResNet) based regression model is proposed and compared with the state of art techniques. Experimental results show that, with a relatively large number of samples our technique is 26.4% better than state of art technique for SSC and 33.7% for firmness. The results of this study indicate the application potential of hyperspectral imaging technique in the SSC and firmness detection, which provides a new option for non-destructive testing of cherry tomato fruit quality in the future. |
format | Online Article Text |
id | pubmed-9108868 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91088682022-05-17 Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation Xiang, Yun Chen, Qijun Su, Zhongjing Zhang, Lu Chen, Zuohui Zhou, Guozhi Yao, Zhuping Xuan, Qi Cheng, Yuan Front Plant Sci Plant Science Cherry tomato (Solanum lycopersicum) is popular with consumers over the world due to its special flavor. Soluble solids content (SSC) and firmness are two key metrics for evaluating the product qualities. In this work, we develop non-destructive testing techniques for SSC and fruit firmness based on hyperspectral images and the corresponding deep learning regression model. Hyperspectral reflectance images of over 200 tomato fruits are derived with the spectrum ranging from 400 to 1,000 nm. The acquired hyperspectral images are corrected and the spectral information are extracted. A novel one-dimensional (1D) convolutional ResNet (Con1dResNet) based regression model is proposed and compared with the state of art techniques. Experimental results show that, with a relatively large number of samples our technique is 26.4% better than state of art technique for SSC and 33.7% for firmness. The results of this study indicate the application potential of hyperspectral imaging technique in the SSC and firmness detection, which provides a new option for non-destructive testing of cherry tomato fruit quality in the future. Frontiers Media S.A. 2022-05-02 /pmc/articles/PMC9108868/ /pubmed/35586212 http://dx.doi.org/10.3389/fpls.2022.860656 Text en Copyright © 2022 Xiang, Chen, Su, Zhang, Chen, Zhou, Yao, Xuan and Cheng. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Xiang, Yun Chen, Qijun Su, Zhongjing Zhang, Lu Chen, Zuohui Zhou, Guozhi Yao, Zhuping Xuan, Qi Cheng, Yuan Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation |
title | Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation |
title_full | Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation |
title_fullStr | Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation |
title_full_unstemmed | Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation |
title_short | Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation |
title_sort | deep learning and hyperspectral images based tomato soluble solids content and firmness estimation |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9108868/ https://www.ncbi.nlm.nih.gov/pubmed/35586212 http://dx.doi.org/10.3389/fpls.2022.860656 |
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