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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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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 |
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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 |
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