View in EDS HTML Full Text PDF Full Text

Estimating Aboveground Biomass of Oilseed Rape by Fusing Point Cloud Voxelization and Vegetation Indices Derived from UAV RGB Imagery.

Saved in:
Bibliographic Details
Title: Estimating Aboveground Biomass of Oilseed Rape by Fusing Point Cloud Voxelization and Vegetation Indices Derived from UAV RGB Imagery.
Authors: Bai, Bingyu1 (AUTHOR), Chen, Tianci1,2 (AUTHOR), Mo, Yanxi2,3 (AUTHOR), Wu, Yushan1 (AUTHOR), Sun, Jiuyue1,2 (AUTHOR), Zou, Qiong3 (AUTHOR), Fu, Shaohong3 (AUTHOR), Li, Yun3 (AUTHOR), Shi, Haoran3 (AUTHOR), Wu, Qiaobo3 (AUTHOR), Yang, Jin3 (AUTHOR), Gong, Wanzhuo3 (AUTHOR) gongwanzhuo@hotmail.com
Source: Remote Sensing. May2026, Vol. 18 Issue 9, p1323. 20p.
Subjects: Biomass estimation, Remote sensing, Rapeseed, Precision farming, Plant biomass, Agricultural remote sensing, Plant indicators
Abstract: Highlights: What are the main findings? The integration of point cloud voxelization with vegetation indices enabled robust estimation of oilseed rape aboveground biomass (AGB), with correlation coefficients exceeding 0.80 between estimated and measured AGB across the entire growth period for all voxel treatments. Among 20 vegetation indices tested, the blue green ratio index (BGI) combined with a cubic regression model under a 45° camera angle and 0.1–m voxel size was identified as the most suitable combination, offering a good balance between estimation performance and analysis time. What are the implications of the main findings? The proposed CVMVI framework offers a non-destructive, efficient, and low-cost method for field-scale crop biomass estimation, facilitating real-time growth monitoring and precision agriculture. The use of larger voxel sizes (e.g., 0.1 m) substantially reduces data processing time without compromising estimation performance, enhancing the practical applicability of UAV-based remote sensing in agricultural production. To support low-cost, non-destructive crop growth monitoring, this study systematically compared different vegetation indices, voxel sizes, and camera angles using a point cloud voxelization approach combined with a vegetation index weighted canopy volume index (CVMVI) to assess aboveground biomass (AGB) in winter oilseed rape (Brassica napus L.). Field experiments were conducted from 2021 to 2024 at the Yangma Experimental Base of the Chengdu Academy of Agricultural and Forestry Sciences. Red, green, blue (RGB) imagery of oilseed rape was acquired using an unmanned aerial vehicle (UAV) during the following five key growth stages: seedling, bolting, flowering, podding, and maturity. Collected images were processed to generate point clouds, which were subsequently voxelized at four resolutions (0.03, 0.05, 0.07, and 0.1 m). CVMVI was constructed by integrating vegetation indices (VIs) derived from the RGB data and the voxelized canopy structural information. Regression models were established between the CVMVI values and field-measured AGB to estimate biomass. Model performance was evaluated using the coefficient of determination (R2), root mean square error (RMSE), and relative error (RE). There were strong correlations (r > 0.80) between the estimated and measured AGB across all voxelization treatments throughout the growth period. Among the 20 VIs tested, regression methods based on the blue green ratio index (BGI), color intensity index, blue red ratio index, vegetative index, and green red ratio index consistently showed superior estimation performance across three consecutive years, demonstrating their good applicability for estimating AGB in oilseed rape under varying agronomic conditions (different varieties, densities, and sowing dates). The cubic regression model CVMBGI performed best under a 45° UAV camera angle, with the highest R2 and lowest RMSE and RE (2021–2022: R2 = 0.864, RMSE = 2414.18 kg/ha, RE = 14.8%; 2022–2023: R2 = 0.754, RMSE = 2550.53 kg/ha, RE = 14.9%; 2023–2024: R2 = 0.863, RMSE = 1953.61 kg/ha, RE = 22.9%). Since the estimation performance showed negligible differences among voxel sizes, and the 0.1–m voxel offered the smallest data volume and shortest analysis time, the CVMBGI model with a 0.1–m voxel was selected as the preferred approach, providing a practical balance between estimation performance and processing demand. These findings highlight the application potential of point cloud voxelization technology for crop biomass estimation. This study proposes a novel, non-destructive, and efficient framework for estimating field crop AGB using low-cost UAV RGB imagery, facilitating the wider adoption of UAV technology in practical agricultural production. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI 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
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 193715354
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Estimating Aboveground Biomass of Oilseed Rape by Fusing Point Cloud Voxelization and Vegetation Indices Derived from UAV RGB Imagery.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Bai%2C+Bingyu%22">Bai, Bingyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Tianci%22">Chen, Tianci</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mo%2C+Yanxi%22">Mo, Yanxi</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Yushan%22">Wu, Yushan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Jiuyue%22">Sun, Jiuyue</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zou%2C+Qiong%22">Zou, Qiong</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fu%2C+Shaohong%22">Fu, Shaohong</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Yun%22">Li, Yun</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shi%2C+Haoran%22">Shi, Haoran</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Qiaobo%22">Wu, Qiaobo</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Jin%22">Yang, Jin</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gong%2C+Wanzhuo%22">Gong, Wanzhuo</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> gongwanzhuo@hotmail.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 9, p1323. 20p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Biomass+estimation%22">Biomass estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Rapeseed%22">Rapeseed</searchLink><br /><searchLink fieldCode="DE" term="%22Precision+farming%22">Precision farming</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+biomass%22">Plant biomass</searchLink><br /><searchLink fieldCode="DE" term="%22Agricultural+remote+sensing%22">Agricultural remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+indicators%22">Plant indicators</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? The integration of point cloud voxelization with vegetation indices enabled robust estimation of oilseed rape aboveground biomass (AGB), with correlation coefficients exceeding 0.80 between estimated and measured AGB across the entire growth period for all voxel treatments. Among 20 vegetation indices tested, the blue green ratio index (BGI) combined with a cubic regression model under a 45° camera angle and 0.1–m voxel size was identified as the most suitable combination, offering a good balance between estimation performance and analysis time. What are the implications of the main findings? The proposed CVMVI framework offers a non-destructive, efficient, and low-cost method for field-scale crop biomass estimation, facilitating real-time growth monitoring and precision agriculture. The use of larger voxel sizes (e.g., 0.1 m) substantially reduces data processing time without compromising estimation performance, enhancing the practical applicability of UAV-based remote sensing in agricultural production. To support low-cost, non-destructive crop growth monitoring, this study systematically compared different vegetation indices, voxel sizes, and camera angles using a point cloud voxelization approach combined with a vegetation index weighted canopy volume index (CVMVI) to assess aboveground biomass (AGB) in winter oilseed rape (Brassica napus L.). Field experiments were conducted from 2021 to 2024 at the Yangma Experimental Base of the Chengdu Academy of Agricultural and Forestry Sciences. Red, green, blue (RGB) imagery of oilseed rape was acquired using an unmanned aerial vehicle (UAV) during the following five key growth stages: seedling, bolting, flowering, podding, and maturity. Collected images were processed to generate point clouds, which were subsequently voxelized at four resolutions (0.03, 0.05, 0.07, and 0.1 m). CVMVI was constructed by integrating vegetation indices (VIs) derived from the RGB data and the voxelized canopy structural information. Regression models were established between the CVMVI values and field-measured AGB to estimate biomass. Model performance was evaluated using the coefficient of determination (R2), root mean square error (RMSE), and relative error (RE). There were strong correlations (r > 0.80) between the estimated and measured AGB across all voxelization treatments throughout the growth period. Among the 20 VIs tested, regression methods based on the blue green ratio index (BGI), color intensity index, blue red ratio index, vegetative index, and green red ratio index consistently showed superior estimation performance across three consecutive years, demonstrating their good applicability for estimating AGB in oilseed rape under varying agronomic conditions (different varieties, densities, and sowing dates). The cubic regression model CVMBGI performed best under a 45° UAV camera angle, with the highest R2 and lowest RMSE and RE (2021–2022: R2 = 0.864, RMSE = 2414.18 kg/ha, RE = 14.8%; 2022–2023: R2 = 0.754, RMSE = 2550.53 kg/ha, RE = 14.9%; 2023–2024: R2 = 0.863, RMSE = 1953.61 kg/ha, RE = 22.9%). Since the estimation performance showed negligible differences among voxel sizes, and the 0.1–m voxel offered the smallest data volume and shortest analysis time, the CVMBGI model with a 0.1–m voxel was selected as the preferred approach, providing a practical balance between estimation performance and processing demand. These findings highlight the application potential of point cloud voxelization technology for crop biomass estimation. This study proposes a novel, non-destructive, and efficient framework for estimating field crop AGB using low-cost UAV RGB imagery, facilitating the wider adoption of UAV technology in practical agricultural production. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193715354
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18091323
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 20
        StartPage: 1323
    Subjects:
      – SubjectFull: Biomass estimation
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Rapeseed
        Type: general
      – SubjectFull: Precision farming
        Type: general
      – SubjectFull: Plant biomass
        Type: general
      – SubjectFull: Agricultural remote sensing
        Type: general
      – SubjectFull: Plant indicators
        Type: general
    Titles:
      – TitleFull: Estimating Aboveground Biomass of Oilseed Rape by Fusing Point Cloud Voxelization and Vegetation Indices Derived from UAV RGB Imagery.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Bai, Bingyu
      – PersonEntity:
          Name:
            NameFull: Chen, Tianci
      – PersonEntity:
          Name:
            NameFull: Mo, Yanxi
      – PersonEntity:
          Name:
            NameFull: Wu, Yushan
      – PersonEntity:
          Name:
            NameFull: Sun, Jiuyue
      – PersonEntity:
          Name:
            NameFull: Zou, Qiong
      – PersonEntity:
          Name:
            NameFull: Fu, Shaohong
      – PersonEntity:
          Name:
            NameFull: Li, Yun
      – PersonEntity:
          Name:
            NameFull: Shi, Haoran
      – PersonEntity:
          Name:
            NameFull: Wu, Qiaobo
      – PersonEntity:
          Name:
            NameFull: Yang, Jin
      – PersonEntity:
          Name:
            NameFull: Gong, Wanzhuo
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1