Optimization of UAV-Based Imaging and Image Processing Orthomosaic and Point Cloud Approaches for Estimating Biomass in a Forage Crop
Forage and field peas provide essential nutrients for livestock diets, and high-quality field peas can influence livestock health and reduce greenhouse gas emissions. Above-ground biomass (AGBM) is one of the vital traits and the primary component of yield in forage pea breeding programs. However, a...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/10/2396 |
| _version_ | 1797495988866252800 |
|---|---|
| author | Worasit Sangjan Rebecca J. McGee Sindhuja Sankaran |
| author_facet | Worasit Sangjan Rebecca J. McGee Sindhuja Sankaran |
| author_sort | Worasit Sangjan |
| collection | DOAJ |
| description | Forage and field peas provide essential nutrients for livestock diets, and high-quality field peas can influence livestock health and reduce greenhouse gas emissions. Above-ground biomass (AGBM) is one of the vital traits and the primary component of yield in forage pea breeding programs. However, a standard method of AGBM measurement is a destructive and labor-intensive process. This study utilized an unmanned aerial vehicle (UAV) equipped with a true-color RGB and a five-band multispectral camera to estimate the AGBM of winter pea in three breeding trials (two seed yields and one cover crop). Three processing techniques—vegetation index (VI), digital surface model (DSM), and 3D reconstruction model from point clouds—were used to extract the digital traits (height and volume) associated with AGBM. The digital traits were compared with the ground reference data (measured plant height and harvested AGBM). The results showed that the canopy volume estimated from the 3D model (alpha shape, <i>α</i> = 1.5) developed from UAV-based RGB imagery’s point clouds provided consistent and high correlation with fresh AGBM (<i>r</i> = 0.78–0.81, <i>p</i> < 0.001) and dry AGBM (<i>r</i> = 0.70–0.81, <i>p</i> < 0.001), compared with other techniques across the three trials. The DSM-based approach (height at 95th percentile) had consistent and high correlation (<i>r</i> = 0.71–0.95, <i>p</i> < 0.001) with canopy height estimation. Using the UAV imagery, the proposed approaches demonstrated the potential for estimating the crop AGBM across winter pea breeding trials. |
| first_indexed | 2024-03-10T01:57:18Z |
| format | Article |
| id | doaj.art-d989021d199f4c74b16b533e65dd4e18 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T01:57:18Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d989021d199f4c74b16b533e65dd4e182023-11-23T12:55:32ZengMDPI AGRemote Sensing2072-42922022-05-011410239610.3390/rs14102396Optimization of UAV-Based Imaging and Image Processing Orthomosaic and Point Cloud Approaches for Estimating Biomass in a Forage CropWorasit Sangjan0Rebecca J. McGee1Sindhuja Sankaran2Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USAUnited States Department of Agriculture-Agricultural Research Service, Grain Legume Genetics and Physiology Research Unit, Washington State University, Pullman, WA 99164, USADepartment of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USAForage and field peas provide essential nutrients for livestock diets, and high-quality field peas can influence livestock health and reduce greenhouse gas emissions. Above-ground biomass (AGBM) is one of the vital traits and the primary component of yield in forage pea breeding programs. However, a standard method of AGBM measurement is a destructive and labor-intensive process. This study utilized an unmanned aerial vehicle (UAV) equipped with a true-color RGB and a five-band multispectral camera to estimate the AGBM of winter pea in three breeding trials (two seed yields and one cover crop). Three processing techniques—vegetation index (VI), digital surface model (DSM), and 3D reconstruction model from point clouds—were used to extract the digital traits (height and volume) associated with AGBM. The digital traits were compared with the ground reference data (measured plant height and harvested AGBM). The results showed that the canopy volume estimated from the 3D model (alpha shape, <i>α</i> = 1.5) developed from UAV-based RGB imagery’s point clouds provided consistent and high correlation with fresh AGBM (<i>r</i> = 0.78–0.81, <i>p</i> < 0.001) and dry AGBM (<i>r</i> = 0.70–0.81, <i>p</i> < 0.001), compared with other techniques across the three trials. The DSM-based approach (height at 95th percentile) had consistent and high correlation (<i>r</i> = 0.71–0.95, <i>p</i> < 0.001) with canopy height estimation. Using the UAV imagery, the proposed approaches demonstrated the potential for estimating the crop AGBM across winter pea breeding trials.https://www.mdpi.com/2072-4292/14/10/2396above-ground biomassunmanned aerial vehiclevegetation indicesdigital surface model3D reconstruction model |
| spellingShingle | Worasit Sangjan Rebecca J. McGee Sindhuja Sankaran Optimization of UAV-Based Imaging and Image Processing Orthomosaic and Point Cloud Approaches for Estimating Biomass in a Forage Crop Remote Sensing above-ground biomass unmanned aerial vehicle vegetation indices digital surface model 3D reconstruction model |
| title | Optimization of UAV-Based Imaging and Image Processing Orthomosaic and Point Cloud Approaches for Estimating Biomass in a Forage Crop |
| title_full | Optimization of UAV-Based Imaging and Image Processing Orthomosaic and Point Cloud Approaches for Estimating Biomass in a Forage Crop |
| title_fullStr | Optimization of UAV-Based Imaging and Image Processing Orthomosaic and Point Cloud Approaches for Estimating Biomass in a Forage Crop |
| title_full_unstemmed | Optimization of UAV-Based Imaging and Image Processing Orthomosaic and Point Cloud Approaches for Estimating Biomass in a Forage Crop |
| title_short | Optimization of UAV-Based Imaging and Image Processing Orthomosaic and Point Cloud Approaches for Estimating Biomass in a Forage Crop |
| title_sort | optimization of uav based imaging and image processing orthomosaic and point cloud approaches for estimating biomass in a forage crop |
| topic | above-ground biomass unmanned aerial vehicle vegetation indices digital surface model 3D reconstruction model |
| url | https://www.mdpi.com/2072-4292/14/10/2396 |
| work_keys_str_mv | AT worasitsangjan optimizationofuavbasedimagingandimageprocessingorthomosaicandpointcloudapproachesforestimatingbiomassinaforagecrop AT rebeccajmcgee optimizationofuavbasedimagingandimageprocessingorthomosaicandpointcloudapproachesforestimatingbiomassinaforagecrop AT sindhujasankaran optimizationofuavbasedimagingandimageprocessingorthomosaicandpointcloudapproachesforestimatingbiomassinaforagecrop |