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Projeto de investigação
Vineyard yield estimation using proximal sensing technologies deployed on an unmanned ground vehicle
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Autores
Publicações
A Multicultivar Approach for Grape Bunch Weight Estimation Using Image Analysis
Publication . Victorino, G.; Poblete-Echeverria, C.; Lopes, C.M.
The determination of bunch features that are relevant for bunch weight estimation is an
important step in automatic vineyard yield estimation using image analysis. The conversion of 2D
image features into mass can be highly dependent on grapevine cultivar, as the bunch morphology
varies greatly. This paper aims to explore the relationships between bunch weight and bunch features
obtained from image analysis considering a multicultivar approach. A set of 192 bunches from four
cultivars, collected at sites located in Portugal and South Africa, were imaged using a conventional
digital RGB camera, followed by image analysis, where several bunch features were extracted, along
with physical measurements performed in laboratory conditions. Image data features were explored
as predictors of bunch weight, individually and in a multiple stepwise regression analysis, which
were then tested on 37% of the data. The results show that the variables bunch area and visible
berries are good predictors of bunch weight (R2 ranging from 0.72 to 0.90); however, the simple
regression lines fitted between these predictors and the response variable presented significantly
different slopes among cultivars, indicating cultivar dependency. The elected multiple regression
model used a combination of four variables: bunch area, bunch perimeter, visible berry number, and
average berry area. The regression analysis between the actual and estimated bunch weight yielded a
R2 = 0.91 on the test set. Our results are an important step towards automatic yield estimation in the
vineyard, as they increase the possibility of applying image-based approaches using a generalized
model, independent of the cultivar
Overcoming the challenge of bunch occlusion by leaves for vineyard yield estimation using image analysis
Publication . Victorino, Gonçalo; Braga, Ricardo; Santos-Victor, José; Lopes, C.M.
Accurate yield estimation is of utmost importance for the entire grape and wine production chain,
yet it remains an extremely challenging process due to high spatial and temporal variability in
vineyards. Recent research has focused on using image analysis for vineyard yield estimation,
with one of the major obstacles being the high degree of occlusion of bunches by leaves. This
work uses canopy features obtained from 2D images (canopy porosity and visible bunch area)
as proxies for estimating the proportion of occluded bunches by leaves to enable automatic
yield estimation on non-disturbed canopies. Data was collected from three grapevine varieties,
and images were captured from 1 m segments at two phenological stages (veraison and full
maturation) in non-defoliated and partially defoliated vines. Visible bunches (bunch exposure;
BE) varied between 16 and 64 %. This percentage was estimated using a multiple regression
model that includes canopy porosity and visible bunch area as predictors, yielding a R2 between
0.70 and 0.84 on a training set composed of 70 % of all data, showing an explanatory power
10 to 43 % higher than when using the predictors individually. A model based on the combined
data set (all varieties and phenological stages) was selected for BE estimation, achieving a R2 =
0.80 on the validation set. This model did not show validation metrics differences when applied
on data collected at veraison or full maturation, suggesting that BE can be accurately estimated
at any stage. Bunch exposure was then used to estimate total bunch area (tBA), showing low
errors (< 10 %) except for the variety Arinto, which presents specific morphological traits such
as large leaves and bunches. Finally, yield estimation computed from estimated tBA presented
a very low error (0.2 %) on the validation data set with pooled data. However, when performed
on every single variety, the simplified approach of area-to-mass conversion was less accurate
for the variety Syrah. The method demonstrated in this work is an important step towards a fully
automated non-invasive yield estimation approach, as it offers a solution to estimate bunches
that are not visible to imaging sensors
Estimativa automática da produção da vinha com recurso a análise de imagem
Publication . Victorino, Gonçalo; Lopes, Carlos M.
Estimar a produção na vinha atempadamente é uma tarefa essencial para uma gestão informada e para uma adequada organização da vindima. No Instituto Superior de Agronomia, Universidade de Lisboa, foi desenvolvido um método não invasivo, baseado em análise de imagem da zona de frutificação, que pode vir a ser uma alternativa aos métodos convencionais de estimativa antecipada da produção vitícola.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
OE
Número da atribuição
SFRH/BD/132305/2017