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Evaluation of an AI-Integrated Laboratory Tool for Estimation of Rice Milling Yield.

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Title: Evaluation of an AI-Integrated Laboratory Tool for Estimation of Rice Milling Yield.
Authors: Olaoni, Samuel O.1 (AUTHOR), Atungulu, Griffiths G.1 (AUTHOR) atungulu@uark.edu
Source: Journal of the ASABE. 2026, Vol. 69 Issue 1, p25-33. 9p.
Subjects: Rice milling, Laboratory equipment & supplies, Rice, Rice quality, Laboratory techniques, Grain milling
Abstract: The article focuses on evaluating the effectiveness of the MachVision rice analyzer, an AI-integrated tool, for estimating head rice yield (HRY) compared to conventional laboratory methods across various U.S. rice cultivars. HRY is a critical metric in determining the commercial value of rice, as it reflects the proportion of whole kernels after milling. The study found that while the MachVision analyzer generally provided consistent HRY estimates, it tended to slightly underestimate values compared to traditional methods, with a mean bias of -3 and a strong correlation (r > 0.90) between the two approaches. The findings suggest that the MachVision analyzer could serve as a reliable, rapid alternative for assessing rice milling quality, although further calibration and validation are necessary to ensure accuracy across different cultivars and milling conditions. [Extracted from the article]
Copyright of Journal of the ASABE is the property of American Society of Agricultural & Biological Engineers 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.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 191768794
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Evaluation of an AI-Integrated Laboratory Tool for Estimation of Rice Milling Yield.
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  Data: <searchLink fieldCode="AR" term="%22Olaoni%2C+Samuel+O%2E%22">Olaoni, Samuel O.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Atungulu%2C+Griffiths+G%2E%22">Atungulu, Griffiths G.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> atungulu@uark.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+ASABE%22">Journal of the ASABE</searchLink>. 2026, Vol. 69 Issue 1, p25-33. 9p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Rice+milling%22">Rice milling</searchLink><br /><searchLink fieldCode="DE" term="%22Laboratory+equipment+%26+supplies%22">Laboratory equipment & supplies</searchLink><br /><searchLink fieldCode="DE" term="%22Rice%22">Rice</searchLink><br /><searchLink fieldCode="DE" term="%22Rice+quality%22">Rice quality</searchLink><br /><searchLink fieldCode="DE" term="%22Laboratory+techniques%22">Laboratory techniques</searchLink><br /><searchLink fieldCode="DE" term="%22Grain+milling%22">Grain milling</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The article focuses on evaluating the effectiveness of the MachVision rice analyzer, an AI-integrated tool, for estimating head rice yield (HRY) compared to conventional laboratory methods across various U.S. rice cultivars. HRY is a critical metric in determining the commercial value of rice, as it reflects the proportion of whole kernels after milling. The study found that while the MachVision analyzer generally provided consistent HRY estimates, it tended to slightly underestimate values compared to traditional methods, with a mean bias of -3 and a strong correlation (r > 0.90) between the two approaches. The findings suggest that the MachVision analyzer could serve as a reliable, rapid alternative for assessing rice milling quality, although further calibration and validation are necessary to ensure accuracy across different cultivars and milling conditions. [Extracted from the article]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of the ASABE is the property of American Society of Agricultural & Biological Engineers 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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      – Type: doi
        Value: 10.13031/ja.16479
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 9
        StartPage: 25
    Subjects:
      – SubjectFull: Rice milling
        Type: general
      – SubjectFull: Laboratory equipment & supplies
        Type: general
      – SubjectFull: Rice
        Type: general
      – SubjectFull: Rice quality
        Type: general
      – SubjectFull: Laboratory techniques
        Type: general
      – SubjectFull: Grain milling
        Type: general
    Titles:
      – TitleFull: Evaluation of an AI-Integrated Laboratory Tool for Estimation of Rice Milling Yield.
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            NameFull: Olaoni, Samuel O.
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            NameFull: Atungulu, Griffiths G.
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              Text: 2026
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              Y: 2026
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