Estimating Aboveground Biomass of Oilseed Rape by Fusing Point Cloud Voxelization and Vegetation Indices Derived from UAV RGB Imagery.
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| Title: | Estimating Aboveground Biomass of Oilseed Rape by Fusing Point Cloud Voxelization and Vegetation Indices Derived from UAV RGB Imagery. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091323 |
