title: Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters.
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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja), Logroño, Spain.
authors.forename: Javier
authors.initials: J
authors.lastname: Tello
authors.affiliation: Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja), Logroño, Spain.|Centro de Agroingeniería, Instituto Valenciano de Investigaciones Agrarias (IVIA), Moncada, Spain.
authors.forename: Sergio
authors.initials: S
authors.lastname: Cubero
authors.affiliation: Centro de Agroingeniería, Instituto Valenciano de Investigaciones Agrarias (IVIA), Moncada, Spain.
authors.forename: José
authors.initials: J
authors.lastname: Blasco
authors.affiliation: Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja), Logroño, Spain.
authors.forename: Javier
authors.initials: J
authors.lastname: Tardaguila
authors.affiliation: Departamento de Ingeniería Gráfica, Universitat Politècnica de València, Valencia, Spain.
authors.forename: Nuria
authors.initials: N
authors.lastname: Aleixos
authors.affiliation: Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja), Logroño, Spain.
authors.forename: Javier
authors.initials: J
authors.lastname: Ibáñez
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D018556:Crops, Agricultural / Q000145:classification / Q000254:growth & development*; D035263:Flowering Tops / Q000145:classification / Q000254:growth & development; D005513:Food Inspection / Q000379:methods*; D063427:Food Quality; D005638:Fruit / Q000145:classification / Q000254:growth & development*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D021621:Imaging, Three-Dimensional; D016014:Linear Models; D010781:Photography; D018547:Plant Stems / Q000145:classification / Q000254:growth & development*; D013030:Spain; D013045:Species Specificity; D027843:Vitis / Q000145:classification / Q000254:growth & development*
keywords: Vitis vinifera L; cluster compactness; cluster shape; cluster size; machine vision