Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards Íñiguez, Rubén Gutiérrez Salcedo, Salvador Precision viticulture Digital agriculture Image analysis Proximal sensing Grapevine Yield assessment has been identified as critical topic for grape and wine industry. Computer vision has been applied for assessing yield, but the accuracy was greatly affected by fruit occlusion affected by leaves and other plant organs. The objective of this work was the consistent, continuous evaluation of the impact of leaf occlusions in different commercial vineyard plots at different defoliation stages. RGB (red, green and blue) images from five Tempranillo (Vitis vinifera L.) vineyards were manually acquired using a digital camera under field conditions at three different levels of defoliation: no defoliation, partial defoliation and full defoliation. Computer vision was used for the automatic detection of different canopy features, and for the calibration of regression equations for the prediction of yield computed per vine segment. Leaf occlusion rate (berry occlusion affected by leaves) was computed by machine vision in no defoliated vineyards. As occlusion rate increased, R 2 between bunch pixels and yield was gradually reduced, ranging from 0.77 in low occlusion, to 0.63. 2021-05-20T10:43:02Z 2021-05-20T10:43:02Z 2021 journal article Íñiguez, R.; Palacios, F.; Barrio, I.; Hernández, I.; Gutiérrez, S.; Tardaguila, J. Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards. Agronomy 2021, 11, 1003. https://doi.org/10.3390/agronomy 11051003 http://hdl.handle.net/10481/68586 10.3390/agronomy11051003 eng http://creativecommons.org/licenses/by/3.0/es/ open access Atribución 3.0 España MDPI