Mestrado em Engenharia de Viticultura e Enologia (Double degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoNowadays, yield estimation represents one of the most important topics in viticulture. It can lead to a better vineyard management and to a better organization of harvesting operations in the vineyard and in the cellar. In recent years, image analysis has become an important tool to improve yield forecast, with the advantages of saving time and being non-invasive. This research aims to estimate the yield of the white cultivar ‘Encruzado’ using visible berry number counted in the images aquired at veraison and near harvest, using a manual RGB camera and the robot VINBOT. Images were collected in laboratory and in the field at the experimental vineyard of the Instituto Superior de Agronomia (ISA) in Lisbon. In the field images the number of visible berries per canopy meter was higher at maturation than at veraison, respectively 72.6 and 66.3. Regarding the percentage of visible berries, 30.2% where visible at veraison and 24.1% at maturation. Concerning percentage of berries occluded by other berries it was observed 28.7% at veraison and 24.3% at maturation. Regression analysis showed that the number of berries in the image explained a very high proportion of bunch weight variability, R2=0.64 at veraison and 0.91 at maturation. Regression analysis also showed that the canopy porosity explained a very high proportion of visible berries variability, R2=0.81 at veraison and 0.88 at maturation. The obtained regression models underestimated the yield with an higher error at veraison than at maturation. This underestimation indicates that the use of visible berry number on the images to estimate yield still needs further research to improve the algorithms accuracyN/
