Assessment of vineyard vigour and yield spatio-temporal variability based on UAV high resolution multispectral images. Ferro, M., V., Catania, P., Miccichè, D., Pisciotta, A., Vallone, M., & Orlando, S. Biosystems Engineering, 231:36-56, Academic Press, 7, 2023. ![Assessment of vineyard vigour and yield spatio-temporal variability based on UAV high resolution multispectral images [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex Accurate, timely assessment of the vineyard on a field scale is essential for successful grape yield and quality. Remote sensing can be an effective and useful monitoring tool, as data from sensors on board Unmanned Aerial Vehicles (UAV) can measure vegetative and reproductive growth and thus directly or indirectly detect variability. Through the images obtained from UAV, the Vegetation Indices (VIs) can be calculated and compared with various agronomic characteristics of the vineyard. The objective of this study was to evaluate the multispectral response of the vineyard in three specific phenological phases and to analyse the spatial distribution of vegetative vigour. A multirotor UAV equipped with a camera featuring multispectral sensors was used. Four VIs namely Normalised Difference Vegetation Index (NDVI), Normalised Difference Red Edge (NDRE), Green Normalised Difference Vegetation Index (GNDVI), Modified Soil Adjusted Vegetation Index (MSAVI), were calculated using the georeferenced orthomosaic UAV images. Computer vision techniques were used to segment these orthoimages to extract only the vegetation canopy pixels. High level of agronomic variability within the vineyard was identified. Pearson's coefficient showed a significant correlation between NDVI and NDRE indices and yield since early phenological stages (r = 0.80 and 0.72 respectively), GNDVI at grape ripening (r = 0.83). Shoot pruning weight (SPW) shows the highest values of correlation (r = 0.84) with NDVI during the phenological stage of berries pea size. Simple linear regression techniques were evaluated using VIs as predictors of the SPW, and accurate predictive results were obtained for NDVI and NDRE with RMSE values of 0.18 and 0.24, respectively. Geostatistical analysis was applied to model the spatial variability of SPW, and thus vineyard vigour. Assessing spatial variability and appreciating the level of vigour enables improved vineyard management by increasing sustainability and production efficiency.
@article{
title = {Assessment of vineyard vigour and yield spatio-temporal variability based on UAV high resolution multispectral images},
type = {article},
year = {2023},
keywords = {Precision viticulture,Shoot pruning weight,Vegetation index},
pages = {36-56},
volume = {231},
month = {7},
publisher = {Academic Press},
day = {1},
id = {963e61b2-4599-34fa-a294-15fd9d6eae60},
created = {2023-10-27T07:33:21.907Z},
accessed = {2023-10-27},
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last_modified = {2023-11-06T09:35:26.806Z},
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abstract = {Accurate, timely assessment of the vineyard on a field scale is essential for successful grape yield and quality. Remote sensing can be an effective and useful monitoring tool, as data from sensors on board Unmanned Aerial Vehicles (UAV) can measure vegetative and reproductive growth and thus directly or indirectly detect variability. Through the images obtained from UAV, the Vegetation Indices (VIs) can be calculated and compared with various agronomic characteristics of the vineyard. The objective of this study was to evaluate the multispectral response of the vineyard in three specific phenological phases and to analyse the spatial distribution of vegetative vigour. A multirotor UAV equipped with a camera featuring multispectral sensors was used. Four VIs namely Normalised Difference Vegetation Index (NDVI), Normalised Difference Red Edge (NDRE), Green Normalised Difference Vegetation Index (GNDVI), Modified Soil Adjusted Vegetation Index (MSAVI), were calculated using the georeferenced orthomosaic UAV images. Computer vision techniques were used to segment these orthoimages to extract only the vegetation canopy pixels. High level of agronomic variability within the vineyard was identified. Pearson's coefficient showed a significant correlation between NDVI and NDRE indices and yield since early phenological stages (r = 0.80 and 0.72 respectively), GNDVI at grape ripening (r = 0.83). Shoot pruning weight (SPW) shows the highest values of correlation (r = 0.84) with NDVI during the phenological stage of berries pea size. Simple linear regression techniques were evaluated using VIs as predictors of the SPW, and accurate predictive results were obtained for NDVI and NDRE with RMSE values of 0.18 and 0.24, respectively. Geostatistical analysis was applied to model the spatial variability of SPW, and thus vineyard vigour. Assessing spatial variability and appreciating the level of vigour enables improved vineyard management by increasing sustainability and production efficiency.},
bibtype = {article},
author = {Ferro, Massimo V. and Catania, Pietro and Miccichè, Daniele and Pisciotta, Antonino and Vallone, Mariangela and Orlando, Santo},
doi = {10.1016/J.BIOSYSTEMSENG.2023.06.001},
journal = {Biosystems Engineering}
}
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