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Study on the Quantitative Damage of Apple Based on Convolutional Neural Network Combined With Mass Compensative Method.

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Title: Study on the Quantitative Damage of Apple Based on Convolutional Neural Network Combined With Mass Compensative Method.
Authors: Li, Bin1 (AUTHOR) libingioe@126.com, Wan, Yi‐rong1 (AUTHOR), Wan, Xia1 (AUTHOR), Ou‐yang, Shang‐tao1 (AUTHOR), Liu, Yan‐de1 (AUTHOR) jxliuyd@163.com
Source: Journal of Food Process Engineering. May2025, Vol. 48 Issue 5, p1-9. 9p.
Subjects: Convolutional neural networks, Fruit packaging, Deep learning, Fruit quality, Prediction models
Abstract: Nondestructive quantitative analysis of fruit damage can not only provide technical support for fruit quality testing, but also provide the theoretical basis for the improvement of fruit packaging and transportation conditions. However, the models of quantitative prediction of fruit damage are susceptible to influence by own factors (size). Therefore, in order to improve the accuracy of quantitative prediction of fruit damage, one‐dimensional convolutional neural network (1D‐CNN) combined with the mass parameter method was proposed. The study results show that the performances of the 1D‐CNN models are improved by 3.4%–7.0% compared to the traditional models. The performances of 1D‐CNN prediction models based on the mass compensation have been improved by 7.5%–10.3% compared with the precompensation. In conclusion, the 1D‐CNN models based on the masscompensation have positive effects in eliminating the influence of apple size on the quantitative prediction models of apple damage. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Food Process Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: Study on the Quantitative Damage of Apple Based on Convolutional Neural Network Combined With Mass Compensative Method.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Bin%22">Li, Bin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> libingioe@126.com</i><br /><searchLink fieldCode="AR" term="%22Wan%2C+Yi‐rong%22">Wan, Yi‐rong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wan%2C+Xia%22">Wan, Xia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ou‐yang%2C+Shang‐tao%22">Ou‐yang, Shang‐tao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Yan‐de%22">Liu, Yan‐de</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jxliuyd@163.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Food+Process+Engineering%22">Journal of Food Process Engineering</searchLink>. May2025, Vol. 48 Issue 5, p1-9. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Fruit+packaging%22">Fruit packaging</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Fruit+quality%22">Fruit quality</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Nondestructive quantitative analysis of fruit damage can not only provide technical support for fruit quality testing, but also provide the theoretical basis for the improvement of fruit packaging and transportation conditions. However, the models of quantitative prediction of fruit damage are susceptible to influence by own factors (size). Therefore, in order to improve the accuracy of quantitative prediction of fruit damage, one‐dimensional convolutional neural network (1D‐CNN) combined with the mass parameter method was proposed. The study results show that the performances of the 1D‐CNN models are improved by 3.4%–7.0% compared to the traditional models. The performances of 1D‐CNN prediction models based on the mass compensation have been improved by 7.5%–10.3% compared with the precompensation. In conclusion, the 1D‐CNN models based on the masscompensation have positive effects in eliminating the influence of apple size on the quantitative prediction models of apple damage. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Food Process Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/jfpe.70128
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 1
    Subjects:
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Fruit packaging
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Fruit quality
        Type: general
      – SubjectFull: Prediction models
        Type: general
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      – TitleFull: Study on the Quantitative Damage of Apple Based on Convolutional Neural Network Combined With Mass Compensative Method.
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            NameFull: Li, Bin
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            NameFull: Wan, Yi‐rong
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            NameFull: Wan, Xia
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            NameFull: Ou‐yang, Shang‐tao
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            NameFull: Liu, Yan‐de
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            – D: 01
              M: 05
              Text: May2025
              Type: published
              Y: 2025
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              Value: 48
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            – TitleFull: Journal of Food Process Engineering
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