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

Rubén Íñiguez,Javier Tardaguila,Fernando Palacios,Salvador Gutiérrez,Inés Hernández,Ignacio Barrio

doi:10.3390/agronomy11051003

Rubén Íñiguez, Javier Tardaguila + Show 4 more

Open Access

https://doi.org/10.3390/agronomy11051003

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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, R2 between bunch pixels and yield was gradually reduced, ranging from 0.77 in low occlusion, to 0.63.

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IntroductionAcademic Editors: Federica Gaiotti and Chiara Pastore
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Grapevines are considered as important crops for economic relevance

Summary

Introduction

Academic Editors: Federica Gaiotti and Chiara Pastore. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Grapevines are considered as important crops for economic relevance. The precise assessment of different relevant grapevine features would lead to better management and more sustainable practices. The objective estimation of vine yield would be very valuable for growers and other actors in the industry [1]. Objective and rapid estimation of the yield components is needed [2,3], conventional methods are destructive, labor-demanding, time-consuming and of low accuracy [2]. New methods for the yield assessment of grapevines are required to replace time-consuming and traditional procedures

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