State of the Art of Monitoring Technologies and Data Processing for Precision Viticulture

Ammoniaci, Marco; Kartsiotis, Simon-Paolo; Perria, Rita; Storchi, Paolo

doi:10.3390/agriculture11030201

Cited by 41 publications

(15 citation statements)

References 43 publications

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“…The applications of remote sensing [4] to agriculture include crop type (i.e., plant species) mapping; crop condition assessment; crop yield estimation; mapping of soil parameters and crop operations (e.g., tillage methods); monitoring the compliance of crop operations to EU regulations; and directives, e.g., "Farm to Fork" and European Green Deal [5][6][7][8][9][10]. The images remotely sensed from Unmanned Aerial Vehicles (UAVs) [3,11,12] or Remotely Piloted Aerial Systems (RPASs) [13], aircrafts [3] and satellites [5,[14][15][16][17][18][19] are used as mapping tools in order to classify crops and examine their health and viability, as well as monitor crop operations.…”

Section: Introductionmentioning

confidence: 99%

Use of Sentinel-2 Satellite for Spatially Variable Rate Fertiliser Management in a Sicilian Vineyard

Comparetti

Silva

2022

Sustainability

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Satellites can be used for producing maps of within-field crop and soil parameters and, consequentially, spatially variable rate crop input application maps. The plant vegetative vigour index (i.e., Normalised Difference Vegetation Index—NDVI) and the leaf water content index (i.e., Normalised Difference Water Index—NDWI) maps were used to study—through both time and space—the phenological phases of two plots, with Syrah and Nero d’Avola grapevine varieties, in a Sicilian vineyard farm, located in Naro (Agrigento, Sicily, Italy). The aim of this work is to produce spatially variable rate nitrogen fertiliser maps to be applied in the two vineyard plots under study as well as to understand when they should be fertilised or not according to their target crop yields. The average plant vegetative vigour and leaf water content of both the plots showed a high temporal and spatial variability during all phenological phases and, according to these results, the optimal fertilisation time should have been 12 April 2021. In fact, this crop operation is aimed at supporting the vegetative activity but must be performed when the soil water and, therefore, the plant leaf water content are high. Therefore, spatially variable rate fertilisation should have been performed around 12 April 2021 in both plots, using previous NDVI maps and taking into consideration two management zones. This work demonstrates the usefulness of remote sensing data as Decision Support Systems (DSS) for nitrogen fertilisation in order to reduce the production cost, environmental impact and climate footprints per kg of produced grapes, according to the European Green Deal challenges.

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Section: Introductionmentioning

confidence: 99%

Use of Sentinel-2 Satellite for Spatially Variable Rate Fertiliser Management in a Sicilian Vineyard

Comparetti

Silva

2022

Sustainability

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“…The collection of georeferenced spatial datasets, such as satellite imagery, allows winegrowers to optimize the decision-making process by showing the yield variability allowing them to take advantage of this variability for applying selective harvesting to increase the quality of the wine [4]. Production of quality wines requires an adequate selection of fruit to be incorporated into the winemaking process [5,6].…”

Section: Introductionmentioning

confidence: 99%

Modeling °Brix and pH in Wine Grapes from Satellite Images in Colchagua Valley, Chile

2021

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To monitor the ripeness and composition of wine grape berries and establish an optimal harvest date, the determination of °Brix and pH is vital. This research studies two harvest seasons of Cabernet Sauvignon wine grapes: 2017 and 2018. Field data were periodically collected to follow the phenological state of the fruits. In parallel, eight bands and four spectral indices from Sentinel-2 image time series were used, which are directly related to the foliage properties and activity, and indirectly to the fruit evolution. They were related to the variables measured from field samples: °Brix and pH. The °Brix models obtained with the spectral indices presented an R2 of 69% and 73% in the 2017 and 2018 seasons, respectively. In pH modeling, the 2017 season had low R2 results, reaching 43%, improving considerably in the 2018 season, reaching 63.8%. Estimated Brix and pH maps were obtained, expressing the spatial variability in the evolution of the fruit, which is useful for zoning the plots and to improve the sampling task prior to harvest. They are therefore a valuable tool to monitor the maturation, to improve the efficiency of harvest and subsequently, the quality of the wine.

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“…Dentro de la industria vitivinícola, Viña Montes, empresa propietaria de los viñedos en los que se ha desarrollado este estudio, tiene un total de 720 hectáreas de vid y exporta un 93% de su producción (Müller 2004;Lima 2015). La recopilación de datasets espaciales georreferenciados, como las imágenes satelitales, permite a los viticultores optimizar el proceso de toma de decisiones minimizando la variabilidad del rendimiento, así como también aprovechar esta variabilidad para aplicar la cosecha selectiva para aumentar la calidad del vino (Ammoniaci et al 2021). La producción de vino de calidad requiere una adecuada selección de fruta para ser incorporada al proceso (Martinez-Casasnovas et al 2010;Perez Quezada 2006).…”

Section: Introductionunclassified

Proceedings - 3rd Congress in Geomatics Engineering - CIGeo

Furones¹,

Esteban²

2021

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The use of medium/high-density LIDAR (Light Detection And Ranging) data for land modelling and DTM (Digital Terrain Model) is becoming more widespread. This level of detail is difficult to achieve with other means or materials. However, the horizontal and vertical geometric accuracy of the LIDAR points obtained, although high, is not homogeneous. Horizontally you can reach precisions around 30-50 cm, while the vertical precision is rarely greater than 10-15 cm. The result of LIDAR flights, are clouds of points very close to each other (30-60 cm) with significant elevation variations, even if the terrain is flat. And this makes the triangulated models TIN (Triangulated Irregular Network) obtained from such LIDAR data especially chaotic. Since contour lines are generated directly from such triangulated models, their appearance shows excessive noise, with excessively broken and rapidly closed on themselves. Getting smoothed contour liness, without decreasing accuracy, is a challenge for terrain model software. In addition, triangulated models obtained from LIDAR data are the basis for future slope maps of the land. And for the same reason explained in the previous paragraph, these slope maps generated from high or medium density LIDAR point clouds are especially heterogeneous. Achieving uniformity and greater adjustment to reality by reducing the natural noise of LIDAR data is another added challenge. In this paper, the problem of excessive noise from LIDAR data of high (around 8 points/m 2 ) and medium density (around 2 points/m 2 ) in the generation of contour lines and terrain slope maps is raised and solutions are proposed to reduce this noise. All this, in the area of specific software for the management of TIN models and GIS (Geographic Information System) and adapting the alternatives proposed by these programmes.

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State of the Art of Monitoring Technologies and Data Processing for Precision Viticulture

Cited by 41 publications

References 43 publications

Use of Sentinel-2 Satellite for Spatially Variable Rate Fertiliser Management in a Sicilian Vineyard

Use of Sentinel-2 Satellite for Spatially Variable Rate Fertiliser Management in a Sicilian Vineyard

Modeling °Brix and pH in Wine Grapes from Satellite Images in Colchagua Valley, Chile

Proceedings - 3rd Congress in Geomatics Engineering - CIGeo

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