Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Cantürk, Meltem, Zabawa, Laura, Pavlic, Diana, Dreier, Ansgar, Klingbeil, Lasse, Kuhlmann, Heiner
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10682715/
https://www.ncbi.nlm.nih.gov/pubmed/38034574
http://dx.doi.org/10.3389/fpls.2023.1244384
_version_ 1785151034047332352
author Cantürk, Meltem
Zabawa, Laura
Pavlic, Diana
Dreier, Ansgar
Klingbeil, Lasse
Kuhlmann, Heiner
author_facet Cantürk, Meltem
Zabawa, Laura
Pavlic, Diana
Dreier, Ansgar
Klingbeil, Lasse
Kuhlmann, Heiner
author_sort Cantürk, Meltem
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10682715
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-106827152023-11-30 UAV-based individual plant detection and geometric parameter extraction in vineyards Cantürk, Meltem Zabawa, Laura Pavlic, Diana Dreier, Ansgar Klingbeil, Lasse Kuhlmann, Heiner Front Plant Sci Plant Science 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. Frontiers Media S.A. 2023-11-14 /pmc/articles/PMC10682715/ /pubmed/38034574 http://dx.doi.org/10.3389/fpls.2023.1244384 Text en Copyright © 2023 Cantürk, Zabawa, Pavlic, Dreier, Klingbeil and Kuhlmann https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Cantürk, Meltem
Zabawa, Laura
Pavlic, Diana
Dreier, Ansgar
Klingbeil, Lasse
Kuhlmann, Heiner
UAV-based individual plant detection and geometric parameter extraction in vineyards
title UAV-based individual plant detection and geometric parameter extraction in vineyards
title_full UAV-based individual plant detection and geometric parameter extraction in vineyards
title_fullStr UAV-based individual plant detection and geometric parameter extraction in vineyards
title_full_unstemmed UAV-based individual plant detection and geometric parameter extraction in vineyards
title_short UAV-based individual plant detection and geometric parameter extraction in vineyards
title_sort uav-based individual plant detection and geometric parameter extraction in vineyards
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10682715/
https://www.ncbi.nlm.nih.gov/pubmed/38034574
http://dx.doi.org/10.3389/fpls.2023.1244384
work_keys_str_mv AT canturkmeltem uavbasedindividualplantdetectionandgeometricparameterextractioninvineyards
AT zabawalaura uavbasedindividualplantdetectionandgeometricparameterextractioninvineyards
AT pavlicdiana uavbasedindividualplantdetectionandgeometricparameterextractioninvineyards
AT dreieransgar uavbasedindividualplantdetectionandgeometricparameterextractioninvineyards
AT klingbeillasse uavbasedindividualplantdetectionandgeometricparameterextractioninvineyards
AT kuhlmannheiner uavbasedindividualplantdetectionandgeometricparameterextractioninvineyards