Evaluation of novel precision viticulture tool for canopy biomass estimation and missing plant detection based on 2.5D and 3D approaches using RGB images acquired by UAV platform.
3D model
Canopy biomass
Missing plants
Precision viticulture
Unmanned aerial vehicle
Journal
Plant methods
ISSN: 1746-4811
Titre abrégé: Plant Methods
Pays: England
ID NLM: 101245798
Informations de publication
Date de publication:
2020
2020
Historique:
received:
30
03
2020
accepted:
22
06
2020
entrez:
9
7
2020
pubmed:
9
7
2020
medline:
9
7
2020
Statut:
epublish
Résumé
The knowledge of vine vegetative status within a vineyard plays a key role in canopy management in order to achieve a correct vine balance and reach the final desired yield/quality. Detailed information about canopy architecture and missing plants distribution provides useful support for farmers/winegrowers to optimize canopy management practices and the replanting process, respectively. In the last decade, there has been a progressive diffusion of UAV (Unmanned Aerial Vehicles) technologies for Precision Viticulture purposes, as fast and accurate methodologies for spatial variability of geometric plant parameters. The aim of this study was to implement an unsupervised and integrated procedure of biomass estimation and missing plants detection, using both the 2.5D-surface and 3D-alphashape methods. Both methods showed good overall accuracy respect to ground truth biomass measurements with high values of R This paper describes a rapid and objective tool for the farmer to promptly identify canopy management strategies and drive replanting decisions. The 3D approach provided results closer to real canopy volume and higher performance in missing plant detection. However, the dense cloud based analysis required more processing time. In a future perspective, given the continuous technological evolution in terms of computing performance, the overcoming of the current limit represented by the pre- and post-processing phases of the large image dataset should mainstream this methodology.
Sections du résumé
BACKGROUND
BACKGROUND
The knowledge of vine vegetative status within a vineyard plays a key role in canopy management in order to achieve a correct vine balance and reach the final desired yield/quality. Detailed information about canopy architecture and missing plants distribution provides useful support for farmers/winegrowers to optimize canopy management practices and the replanting process, respectively. In the last decade, there has been a progressive diffusion of UAV (Unmanned Aerial Vehicles) technologies for Precision Viticulture purposes, as fast and accurate methodologies for spatial variability of geometric plant parameters. The aim of this study was to implement an unsupervised and integrated procedure of biomass estimation and missing plants detection, using both the 2.5D-surface and 3D-alphashape methods.
RESULTS
RESULTS
Both methods showed good overall accuracy respect to ground truth biomass measurements with high values of R
CONCLUSIONS
CONCLUSIONS
This paper describes a rapid and objective tool for the farmer to promptly identify canopy management strategies and drive replanting decisions. The 3D approach provided results closer to real canopy volume and higher performance in missing plant detection. However, the dense cloud based analysis required more processing time. In a future perspective, given the continuous technological evolution in terms of computing performance, the overcoming of the current limit represented by the pre- and post-processing phases of the large image dataset should mainstream this methodology.
Identifiants
pubmed: 32636922
doi: 10.1186/s13007-020-00632-2
pii: 632
pmc: PMC7333307
doi:
Types de publication
Journal Article
Langues
eng
Pagination
91Informations de copyright
© The Author(s) 2020.
Déclaration de conflit d'intérêts
Competing interestsThe authors declare that they have no competing interests.
Références
PLoS One. 2015 Jun 24;10(6):e0130479
pubmed: 26107174