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Article

Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards

1
Televitis Research Group, University of La Rioja, 26006 Logroño, Spain
2
Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Federica Gaiotti and Chiara Pastore
Agronomy 2021, 11(5), 1003; https://doi.org/10.3390/agronomy11051003
Received: 19 April 2021 / Revised: 11 May 2021 / Accepted: 12 May 2021 / Published: 18 May 2021
(This article belongs to the Special Issue Sustainable Viticulture Production and Vineyard Management Practices)
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, R2 between bunch pixels and yield was gradually reduced, ranging from 0.77 in low occlusion, to 0.63. View Full-Text
Keywords: precision viticulture; digital agriculture; image analysis; proximal sensing; grapevine precision viticulture; digital agriculture; image analysis; proximal sensing; grapevine
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MDPI and ACS Style

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/agronomy11051003

AMA Style

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(5):1003. https://doi.org/10.3390/agronomy11051003

Chicago/Turabian Style

Íñiguez, Rubén, Fernando Palacios, Ignacio Barrio, Inés Hernández, Salvador Gutiérrez, and Javier Tardaguila. 2021. "Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards" Agronomy 11, no. 5: 1003. https://doi.org/10.3390/agronomy11051003

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