Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards
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...
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| Format: | Article |
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MDPI AG
2021-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/9/3083 |
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| author | Jorge Torres-Sánchez Francisco Javier Mesas-Carrascosa Luis-Gonzaga Santesteban Francisco Manuel Jiménez-Brenes Oihane Oneka Ana Villa-Llop Maite Loidi Francisca López-Granados |
| author_facet | Jorge Torres-Sánchez Francisco Javier Mesas-Carrascosa Luis-Gonzaga Santesteban Francisco Manuel Jiménez-Brenes Oihane Oneka Ana Villa-Llop Maite Loidi Francisca López-Granados |
| author_sort | Jorge Torres-Sánchez |
| collection | DOAJ |
| description | 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 R<sup>2</sup> 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 R<sup>2</sup> 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. |
| first_indexed | 2024-03-10T11:52:24Z |
| format | Article |
| id | doaj.art-be5e521af8f8466f858ff08fb59febe0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:52:24Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-be5e521af8f8466f858ff08fb59febe02023-11-21T17:38:40ZengMDPI AGSensors1424-82202021-04-01219308310.3390/s21093083Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in VineyardsJorge Torres-Sánchez0Francisco Javier Mesas-Carrascosa1Luis-Gonzaga Santesteban2Francisco Manuel Jiménez-Brenes3Oihane Oneka4Ana Villa-Llop5Maite Loidi6Francisca López-Granados7Grupo Imaping, Instituto de Agricultura Sostenible-CSIC, 14004 Córdoba, SpainDepartamento de Ingeniería Gráfica y Geomática, Campus de Rabanales, Universidad de Córdoba, 14071 Córdoba, SpainDepartamento de Agronomía, Biotecnología y Alimentación, Universidad Pública de Navarra, 31006 Pamplona, SpainGrupo Imaping, Instituto de Agricultura Sostenible-CSIC, 14004 Córdoba, SpainDepartamento de Agronomía, Biotecnología y Alimentación, Universidad Pública de Navarra, 31006 Pamplona, SpainDepartamento de Agronomía, Biotecnología y Alimentación, Universidad Pública de Navarra, 31006 Pamplona, SpainDepartamento de Agronomía, Biotecnología y Alimentación, Universidad Pública de Navarra, 31006 Pamplona, SpainGrupo Imaping, Instituto de Agricultura Sostenible-CSIC, 14004 Córdoba, SpainYield 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 R<sup>2</sup> 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 R<sup>2</sup> 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.https://www.mdpi.com/1424-8220/21/9/3083fruit detectionremote sensingprecision viticulturecolor thresholdingunsupervised and automated analysis |
| spellingShingle | Jorge Torres-Sánchez Francisco Javier Mesas-Carrascosa Luis-Gonzaga Santesteban Francisco Manuel Jiménez-Brenes Oihane Oneka Ana Villa-Llop Maite Loidi Francisca López-Granados Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards Sensors fruit detection remote sensing precision viticulture color thresholding unsupervised and automated analysis |
| title | Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards |
| title_full | Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards |
| title_fullStr | Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards |
| title_full_unstemmed | Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards |
| title_short | Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards |
| title_sort | grape cluster detection using uav photogrammetric point clouds as a low cost tool for yield forecasting in vineyards |
| topic | fruit detection remote sensing precision viticulture color thresholding unsupervised and automated analysis |
| url | https://www.mdpi.com/1424-8220/21/9/3083 |
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