Grapevine Canopy Volume Estimation from UAV Photogrammetric Point Clouds at Different Flight Heights

Abstract

Vegetation volume is a useful indicator for assessing canopy structure and supporting vineyard management tasks such as foliar applications and canopy management. The photogrammetric processing of imagery acquired using unmanned aerial vehicles (UAVs) enables the generation of dense point clouds suitable for estimating canopy volume, although point cloud quality depends on spatial resolution, which is influenced by flight height. This study evaluates the effect of three flight heights (30 m, 60 m, and 100 m) on grapevine canopy volume estimation using convex hull, alpha shape, and voxel-based models. UAV-based RGB imagery and field measurements were collected during three periods at different phenological stages in an experimental vineyard. The strongest agreement with field-measured volume occurred at 30 m, where point density was highest. Envelope-based methods showed reduced performance at higher flight heights, while voxel-based grids remained more stable when voxel size was adapted to point density. Estimator behavior also varied with canopy architecture and development. The results indicate appropriate parameter choices for different flight heights and confirm that UAV-based RGB imagery can provide reliable grapevine canopy volume estimates.

Plant phenotyping relevance

UAV画像からブドウ樹のキャノピー体積を推定する手法を、飛行高度・推定モデル間で評価し、実測値と比較しているため、植物形質取得法の技術的検証が中心です。

Code and data availability

The supplied blocks describe UAV RGB imagery, photogrammetric point clouds, field-measured canopy volumes, and MATLAB/R analysis pipelines for grapevine canopy volume estimation, but contain no data availability statement, repository deposit, or authors' public URL for the imagery, point clouds, field measurements, or

No evidence-backed public reproduction asset is currently recorded.