@misc{10481/68586,
year = {2021},
url = {http://hdl.handle.net/10481/68586},
abstract = {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.},
organization = {University of La Rioja},
publisher = {MDPI},
keywords = {Precision viticulture},
keywords = {Digital agriculture},
keywords = {Image analysis},
keywords = {Proximal sensing},
keywords = {Grapevine},
title = {Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards},
doi = {10.3390/agronomy11051003},
author = {Íñiguez, Rubén and Gutiérrez Salcedo, Salvador},
}