High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability
Remotesensing techniques can help reduce time and resources spent collecting samples of crops and analyzing quality variables. The main objective of this work was to demonstrate that it is possible to obtain information on the distribution of must quality variables from conventional photographs. Geo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/11/4/655 |
| _version_ | 1797539573612412928 |
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| author | Marta García-Fernández Enoc Sanz-Ablanedo José Ramón Rodríguez-Pérez |
| author_facet | Marta García-Fernández Enoc Sanz-Ablanedo José Ramón Rodríguez-Pérez |
| author_sort | Marta García-Fernández |
| collection | DOAJ |
| description | Remotesensing techniques can help reduce time and resources spent collecting samples of crops and analyzing quality variables. The main objective of this work was to demonstrate that it is possible to obtain information on the distribution of must quality variables from conventional photographs. Georeferenced berry samples were collected and analyzed in the laboratory, and RGB images were taken using a low-cost drone from which an orthoimage was made. Transformation equations were calculated to obtain absolute reflectances for the different bands and to calculate 10 vegetation indices plus two new proposed indices. Correlations for the 12 indices with values for 15 must quality variables were calculated in terms of Pearson’s correlation coefficients. Significant correlations were obtained for 100-berries weight (0.77), malic acid (−0.67), alpha amino nitrogen (−0.59), phenolic maturation index (0.69), and the total polyphenol index (0.62), with 100-berries weight and the total polyphenol index obtaining the best results in the proposed RGB-based vegetation index 2 and RGB-based vegetation index 3. Our findings indicate that must variables important for the production of quality wines can be related to the RGB bands in conventional digital images, potentially improving and aiding management and increasing productivity. |
| first_indexed | 2024-03-10T12:47:45Z |
| format | Article |
| id | doaj.art-c19f4e91c76a47439e364b5964723a16 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T12:47:45Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-c19f4e91c76a47439e364b5964723a162023-11-21T13:22:51ZengMDPI AGAgronomy2073-43952021-03-0111465510.3390/agronomy11040655High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality VariabilityMarta García-Fernández0Enoc Sanz-Ablanedo1José Ramón Rodríguez-Pérez2Grupo de Investigación en Geomática e Ingeniería Cartográfica (GEOINCA), Universidad de León, Avenida de Astorga sn, 24401 Ponferrada, León, SpainGrupo de Investigación en Geomática e Ingeniería Cartográfica (GEOINCA), Universidad de León, Avenida de Astorga sn, 24401 Ponferrada, León, SpainGrupo de Investigación en Geomática e Ingeniería Cartográfica (GEOINCA), Universidad de León, Avenida de Astorga sn, 24401 Ponferrada, León, SpainRemotesensing techniques can help reduce time and resources spent collecting samples of crops and analyzing quality variables. The main objective of this work was to demonstrate that it is possible to obtain information on the distribution of must quality variables from conventional photographs. Georeferenced berry samples were collected and analyzed in the laboratory, and RGB images were taken using a low-cost drone from which an orthoimage was made. Transformation equations were calculated to obtain absolute reflectances for the different bands and to calculate 10 vegetation indices plus two new proposed indices. Correlations for the 12 indices with values for 15 must quality variables were calculated in terms of Pearson’s correlation coefficients. Significant correlations were obtained for 100-berries weight (0.77), malic acid (−0.67), alpha amino nitrogen (−0.59), phenolic maturation index (0.69), and the total polyphenol index (0.62), with 100-berries weight and the total polyphenol index obtaining the best results in the proposed RGB-based vegetation index 2 and RGB-based vegetation index 3. Our findings indicate that must variables important for the production of quality wines can be related to the RGB bands in conventional digital images, potentially improving and aiding management and increasing productivity.https://www.mdpi.com/2073-4395/11/4/655remotesensingdroneRGB imageryspectral indexvineyard zoningmust quality variable |
| spellingShingle | Marta García-Fernández Enoc Sanz-Ablanedo José Ramón Rodríguez-Pérez High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability Agronomy remotesensing drone RGB imagery spectral index vineyard zoning must quality variable |
| title | High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability |
| title_full | High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability |
| title_fullStr | High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability |
| title_full_unstemmed | High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability |
| title_short | High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability |
| title_sort | high resolution drone acquired rgb imagery to estimate spatial grape quality variability |
| topic | remotesensing drone RGB imagery spectral index vineyard zoning must quality variable |
| url | https://www.mdpi.com/2073-4395/11/4/655 |
| work_keys_str_mv | AT martagarciafernandez highresolutiondroneacquiredrgbimagerytoestimatespatialgrapequalityvariability AT enocsanzablanedo highresolutiondroneacquiredrgbimagerytoestimatespatialgrapequalityvariability AT joseramonrodriguezperez highresolutiondroneacquiredrgbimagerytoestimatespatialgrapequalityvariability |