Predicting Grape Yield with Vine Canopy Morphology Analysis from 3D Point Clouds Generated by UAV Imagery

Abstract

With its ability to estimate yield, winemakers may better manage their vineyards and obtain important insights into the possible crop. The proper estimation of grape output is contingent upon an accurate evaluation of the morphology of the vine canopy, as this has a substantial impact on the final product. This study’s main goals were to gather canopy morphology data using a sophisticated 3D model and assess how well different morphology characteristics predicted yield results. An unmanned aerial vehicle (UAV) with an RGB camera was used in the vineyards of Topoľčianky, Slovakia, to obtain precise orthophotos of individual vine rows. Following the creation of an extensive three-dimensional (3D) model of the assigned region, a thorough examination was carried out to determine many canopy characteristics, including thickness, side section dimensions, volume, and surface area. According to the study, the best combination for predicting grape production was the side section and thickness. Using more than one morphological parameter is advised for a more precise yield estimate as opposed to depending on only one.

Plant phenotyping relevance

UAV画像から3Dモデルを作成し、ブドウ樹冠の形態形質（厚さ、断面寸法、体積、表面積）を抽出・評価する手法が研究の中心であり、収量予測へ応用している。

Code and data availability

The article describes UAV image collection, 3D point cloud processing (Agisoft Metashape, CloudCompare, ArcMap), grape yield measurements, and regression equations, but contains no data availability statement, no public dataset or image deposit, and no author code/scripts or workflows with a public URL. Only the paper,

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