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Research on identification and extraction of crop plants in plateau mountainous areas based on multi-dimensional features.

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Title: Research on identification and extraction of crop plants in plateau mountainous areas based on multi-dimensional features.
Authors: Yin, Linjiang1 (AUTHOR), Zhou, Zhongfa2,3 (AUTHOR) fa6897@163.com, Zhao, Weiquan1,2,3 (AUTHOR), Liao, Yanmei4 (AUTHOR), Huang, Denghong2,3 (AUTHOR), Li, Wei1 (AUTHOR)
Source: International Journal of Remote Sensing. Jan2025, Vol. 46 Issue 1, p77-104. 28p.
Subjects: Standard deviations, Plant identification, Point cloud, Image registration, Crops
Abstract: To address poor crop extraction results in mountainous regions using single-feature data in previous research, this study employed a quadcopter to capture aerial orthophoto imagery and image-matching point cloud data from a pitaya cultivation site in the rugged mountainous terrain of southwestern China. The authors identified three critical features: the visible-band difference vegetation index (VDVI), excess green – excess red (ExG-ExR), and canopy height model (CHM) and then integrated them to build a multi-dimensional feature dataset, namely VDVI+CHM and ExG-ExR+CHM. Through a rule-based object-oriented technique, they conducted identification extraction specifically for pitayas plants. The study yielded impressive extraction accuracies, with VDVI, ExG-ExR, CHM segmentation, VDVI+CHM, and ExG-ExR+CHM achieving overall accuracies of 92.34%, 91.05%, 89.08%, 97.56%, and 96.86%, respectively. Furthermore, to validate the accuracy of the extraction results, a regression analysis was conducted to compare the actual canopy area of the pitayas plants determined through human-computer interaction with the extraction results. The root mean square error (RMSE) for VDVI+CHM and ExG-ExR+CHM were found to be 18 dm2 and 25 dm2, respectively, while the coefficient of determination (R2) was 0.81 and 0.67, respectively. Notably, the comparative analysis revealed that VDVI + CHM, which fused multi-dimensional features, exhibited the highest recognition accuracy, demonstrating that integrating multi-dimensional plant features effectively enhanced the accuracy of pitaya plant identification and extraction. By overcoming the limitations of single spectral or spatial structural features, this approach provides valuable insights into the identification and extraction of characteristic economic crops in mountainous regions. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd 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: 181986005
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Research on identification and extraction of crop plants in plateau mountainous areas based on multi-dimensional features.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yin%2C+Linjiang%22">Yin, Linjiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Zhongfa%22">Zhou, Zhongfa</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> fa6897@163.com</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Weiquan%22">Zhao, Weiquan</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liao%2C+Yanmei%22">Liao, Yanmei</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Denghong%22">Huang, Denghong</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Wei%22">Li, Wei</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Remote+Sensing%22">International Journal of Remote Sensing</searchLink>. Jan2025, Vol. 46 Issue 1, p77-104. 28p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+identification%22">Plant identification</searchLink><br /><searchLink fieldCode="DE" term="%22Point+cloud%22">Point cloud</searchLink><br /><searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink><br /><searchLink fieldCode="DE" term="%22Crops%22">Crops</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To address poor crop extraction results in mountainous regions using single-feature data in previous research, this study employed a quadcopter to capture aerial orthophoto imagery and image-matching point cloud data from a pitaya cultivation site in the rugged mountainous terrain of southwestern China. The authors identified three critical features: the visible-band difference vegetation index (VDVI), excess green – excess red (ExG-ExR), and canopy height model (CHM) and then integrated them to build a multi-dimensional feature dataset, namely VDVI+CHM and ExG-ExR+CHM. Through a rule-based object-oriented technique, they conducted identification extraction specifically for pitayas plants. The study yielded impressive extraction accuracies, with VDVI, ExG-ExR, CHM segmentation, VDVI+CHM, and ExG-ExR+CHM achieving overall accuracies of 92.34%, 91.05%, 89.08%, 97.56%, and 96.86%, respectively. Furthermore, to validate the accuracy of the extraction results, a regression analysis was conducted to compare the actual canopy area of the pitayas plants determined through human-computer interaction with the extraction results. The root mean square error (RMSE) for VDVI+CHM and ExG-ExR+CHM were found to be 18 dm2 and 25 dm2, respectively, while the coefficient of determination (R2) was 0.81 and 0.67, respectively. Notably, the comparative analysis revealed that VDVI + CHM, which fused multi-dimensional features, exhibited the highest recognition accuracy, demonstrating that integrating multi-dimensional plant features effectively enhanced the accuracy of pitaya plant identification and extraction. By overcoming the limitations of single spectral or spatial structural features, this approach provides valuable insights into the identification and extraction of characteristic economic crops in mountainous regions. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd 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:
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    Identifiers:
      – Type: doi
        Value: 10.1080/01431161.2024.2391080
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 28
        StartPage: 77
    Subjects:
      – SubjectFull: Standard deviations
        Type: general
      – SubjectFull: Plant identification
        Type: general
      – SubjectFull: Point cloud
        Type: general
      – SubjectFull: Image registration
        Type: general
      – SubjectFull: Crops
        Type: general
    Titles:
      – TitleFull: Research on identification and extraction of crop plants in plateau mountainous areas based on multi-dimensional features.
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          Name:
            NameFull: Yin, Linjiang
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            NameFull: Zhou, Zhongfa
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            NameFull: Zhao, Weiquan
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            NameFull: Liao, Yanmei
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            NameFull: Huang, Denghong
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            NameFull: Li, Wei
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            – D: 01
              M: 01
              Text: Jan2025
              Type: published
              Y: 2025
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              Value: 46
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            – TitleFull: International Journal of Remote Sensing
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