Mestrado em Engenharia de Viticultura e Enologia (Double Degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoYield estimation can lead to difficulties in the vineyard and winery, if it is done inaccurately following
wrong procedures, doing a non-representative sampling or for the human error. Moreover, the
traditional yield estimation methods are time consuming and destructive because they need
someone that goes into the vineyard to count the yield components and that take out from the
vineyard inflorescence or bunches to count and weight the flowers and the berries. To avoid these
problems and the errors that can occur on this way, the development and application of new and
innovative techniques to estimate the yield through the analysis of RGB images taken under field
conditions are under study from different groups of research.
In our research work we’ve studied the application of counting the yield components in the images
throughout all the growing season. Furthermore, we’ve studied two different algorithms that starting
from the survey of canopy porosity and/or visible bunches area, can help to do an estimation of the
yield.
The most promising yield estimation, based on the counting of the yield components done through
image analysis, was found to be at the phenological stage of four leaves out, which shown a mean
absolute percent error (MA%E) of 32 ± 2% and an correlaion coefficient (r Obs,Est) between observed
and estimated shoots of 0.62.
The two algorithms used different models: for estimating the area of the bunches covered by leaves
and to estimate the weight of the bunches per linear canopy meter. When the area of the bunches
without leaf occlusion was estimated, an average percentage of occlusion generated by the bunches
on the other bunches of 8%, 6% and 12% respectively at pea size, veraison and maturation, was
used to estimate the total area of the bunches. When the total area of the bunches per linear canopy
meter was estimated the two models to estimate the grape weight were used. Finally, to estimate
the weight at harvest, the growth factors of 6.6 and 1.7 respectively, at pea size and veraison were
used. The first algorithm shown a MA%E, between the estimated and observed values of yield, of -
33.59%, -9.24% and -11.25%, instead the second algorithm shown a MA%E of -6.81%, -1.35% and
0.01% respectively at pea-size, veraison and maturationN/
