UAV-based individual plant detection and geometric parameter extraction in vineyards

Abstract

Accurately characterizing vineyard parameters is crucial for precise vineyard management and breeding purposes. Various macroscopic vineyard parameters are required to make informed management decisions, such as pesticide application, defoliation strategies, and determining optimal sugar content in each berry by assessing biomass. In this paper, we present a novel approach that utilizes point cloud data to detect trunk positions and extract macroscopic vineyard characteristics, including plant height, canopy width, and canopy volume. Our approach relies solely on geometric features and is compatible with different training systems and data collected using various 3D sensors. To evaluate the effectiveness and robustness of our proposed approach, we conducted extensive experiments on multiple grapevine rows trained in two different systems. Our method provides more comprehensive canopy characteristics than traditional manual measurements, which are not representative throughout the row. The experimental results demonstrate the accuracy and efficiency of our method in extracting vital macroscopic vineyard characteristics, providing valuable insights for yield monitoring, grape quality optimization, and strategic interventions to enhance vineyard productivity and sustainability.

Plant phenotyping relevance

ブドウ個体を検出し、点群から樹高・樹冠幅・樹冠体積を抽出する手法を開発・評価しており、植物形質の取得が研究の中心である。

Code and data availability

The supplied blocks describe UAV-derived point cloud datasets and a Matlab/CloudCompare pipeline for vineyard phenotyping, but contain no data or code availability statement, no public repository, and no URL. No paper-specific public asset can be identified.

No evidence-backed public reproduction asset is currently recorded.