Optimization of UAV-Based Imaging and Image Processing Orthomosaic and Point Cloud Approaches for Estimating Biomass in a Forage Crop

Abstract

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, α = 1.5) developed from UAV-based RGB imagery’s point clouds provided consistent and high correlation with fresh AGBM (r = 0.78–0.81, p

Plant phenotyping relevance

UAV画像・点群・3D再構成を用いて、エンドウの草高・体積から地上部バイオマスを推定し、地上基準値と比較検証する手法研究であり、表現型取得・抽出が中心である。

Code and data availability

The supplied blocks describe UAV imagery, ground-reference biomass data, and processing pipelines (Pix4Dmapper, QGIS, Python with Rasterio/Rasterstats, WhiteboxTools, MATLAB), but contain no data availability statement, public deposit, or author-hosted code/dataset URL. All cited URLs are generic third-party tools/libr

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