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Título

Assessment of grape cluster yield components based on 3D descriptors using stereo vision

AutorIvorra, E.; Sánchez-Alcázar, José Antonio ; Camarasa, J. G.; Diago, Maria P. ; Tardáguila, Javier
Palabras claveStereo-vision
Cluster yield components
3D descriptors
Grape quality
Non-invasive technologies
Vitis vinifera L
Fecha de publicaciónabr-2015
EditorElsevier
CitaciónFood Control 50: 273-282 (2015)
Resumen© 2014 Elsevier Ltd. Wine quality depends mostly on the features of the grapes it is made from. Cluster and berry morphology are key factors in determining grape and wine quality. However, current practices for grapevine quality estimation require time-consuming destructive analysis or largely subjective judgment by experts.The purpose of this paper is to propose a three-dimensional computer vision approach to assessing grape yield components based on new 3D descriptors. To achieve this, firstly a partial three-dimensional model of the grapevine cluster is extracted using stereo vision. After that a number of grapevine quality components are predicted using SVM models based on new 3D descriptors. Experiments confirm that this approach is capable of predicting the main cluster yield components, which are related to quality, such as cluster compactness and berry size (R2 > 0.80, p < 0.05). In addition, other yield components: cluster volume, total berry weight and number of berries, were also estimated using SVM models, obtaining prediction R2 of 0.82, 0.83 and 0.71, respectively.
Versión del editorhttp://doi.org/10.1016/j.foodcont.2014.09.004
URIhttp://hdl.handle.net/10261/144430
DOI10.1016/j.foodcont.2014.09.004
Identificadoresissn: 0956-7135
Aparece en las colecciones: (ICVV) Artículos
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