Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards Article Swipe
Jorge Torres‐Sánchez
,
Francisco Javier Mesas‐Carrascosa
,
L.G. Santesteban
,
Francisco Manuel Jiménez-Brenes
,
Oihane Oneka
,
Ana Villa-Llop
,
Maite Loidi
,
Francisca López Granados
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21093083
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21093083
Yield prediction is crucial for the management of harvest and scheduling wine production operations. Traditional yield prediction methods rely on manual sampling and are time-consuming, making it difficult to handle the intrinsic spatial variability of vineyards. There have been significant advances in automatic yield estimation in vineyards from on-ground imagery, but terrestrial platforms have some limitations since they can cause soil compaction and have problems on sloping and ploughed land. The analysis of photogrammetric point clouds generated with unmanned aerial vehicles (UAV) imagery has shown its potential in the characterization of woody crops, and the point color analysis has been used for the detection of flowers in almond trees. For these reasons, the main objective of this work was to develop an unsupervised and automated workflow for detection of grape clusters in red grapevine varieties using UAV photogrammetric point clouds and color indices. As leaf occlusion is recognized as a major challenge in fruit detection, the influence of partial leaf removal in the accuracy of the workflow was assessed. UAV flights were performed over two commercial vineyards with different grape varieties in 2019 and 2020, and the photogrammetric point clouds generated from these flights were analyzed using an automatic and unsupervised algorithm developed using free software. The proposed methodology achieved R2 values higher than 0.75 between the harvest weight and the projected area of the points classified as grapes in vines when partial two-sided removal treatment, and an R2 of 0.82 was achieved in one of the datasets for vines with untouched full canopy. The accuracy achieved in grape detection opens the door to yield prediction in red grape vineyards. This would allow the creation of yield estimation maps that will ease the implementation of precision viticulture practices. To the authors’ knowledge, this is the first time that UAV photogrammetric point clouds have been used for grape clusters detection.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s21093083
- https://www.mdpi.com/1424-8220/21/9/3083/pdf?version=1619689508
- OA Status
- gold
- Cited By
- 40
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3159702250
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3159702250Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s21093083Digital Object Identifier
- Title
-
Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in VineyardsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-04-28Full publication date if available
- Authors
-
Jorge Torres‐Sánchez, Francisco Javier Mesas‐Carrascosa, L.G. Santesteban, Francisco Manuel Jiménez-Brenes, Oihane Oneka, Ana Villa-Llop, Maite Loidi, Francisca López GranadosList of authors in order
- Landing page
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https://doi.org/10.3390/s21093083Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/21/9/3083/pdf?version=1619689508Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/21/9/3083/pdf?version=1619689508Direct OA link when available
- Concepts
-
Photogrammetry, Point cloud, Ground sample distance, Vine, Remote sensing, Workflow, Environmental science, Vineyard, Computer science, Artificial intelligence, Geography, Pixel, Horticulture, Database, Biology, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
40Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 11, 2023: 12, 2022: 9, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
41Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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