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Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters
Author(s) -
Tello Javier,
Cubero Sergio,
Blasco José,
Tardaguila Javier,
Aleixos Nuria,
Ibáñez Javier
Publication year - 2016
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.7675
Subject(s) - cluster (spacecraft) , computer science , artificial intelligence , wine , volume (thermodynamics) , set (abstract data type) , table (database) , pattern recognition (psychology) , mathematics , data mining , food science , chemistry , physics , quantum mechanics , programming language
Abstract BACKGROUND Grapevine cluster morphology influences the quality and commercial value of wine and table grapes. It is routinely evaluated by subjective and inaccurate methods that do not meet the requirements set by the food industry. Novel two‐dimensional ( 2D ) and three‐dimensional ( 3D ) machine vision technologies emerge as promising tools for its automatic and fast evaluation.RESULTS The automatic evaluation of cluster length, width and elongation was successfully achieved by the analysis of 2D images, significant and strong correlations with the manual methods being found ( r = 0.959, 0.861 and 0.852, respectively). The classification of clusters according to their shape can be achieved by evaluating their conicity in different sections of the cluster. The geometric reconstruction of the morphological volume of the cluster from 2D features worked better than the direct 3D laser scanning system, showing a high correlation ( r = 0.956) with the manual approach (water displacement method). In addition, we constructed and validated a simple linear regression model for cluster compactness estimation. It showed a high predictive capacity for both the training and validation subsets of clusters ( R 2 = 84.5 and 71.1%, respectively). CONCLUSION The methodologies proposed in this work provide continuous and accurate data for the fast and objective characterisation of cluster morphology. © 2016 Society of Chemical Industry