Grupo de Ingeniería Estadística Multivariante GIEM

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Author: Prats Montalbán, José Manuel × Subject: EXPRESION GRAFICA EN LA INGENIERIA ×

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Now showing 1 - 3 of 3
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    A new method for assessment of bunch compactness using automated image analysis
    (Wiley, 2015-02) Cubero García, Sergio; Diago, M. P; Blasco Ivars, José; Tardáguila Laso, Javier; Prats Montalbán, José Manuel; Ibanez, J.; Tello, J.; Aleixos Borrás, María Nuria; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Dpto. de Ingeniería Gráfica; Escuela Técnica Superior de Ingeniería Industrial; Grupo de Ingeniería Estadística Multivariante GIEM; Ministerio de Ciencia e Innovación; Universitat Politècnica de València
    Background and AimsBunch compactness is a key feature determining grape and wine composition because tight bunches show a less homogeneous ripening, and are prone to greater fungal disease incidence. The Organisation Internationale de la Vigne et du Vin descriptor, the most recent method for the assessment of bunch compactness, requires visual inspection and trained evaluators, and provides subjective and qualitative values. The aim of this work was to develop a methodology based on image analysis to determine bunch compactness in a non-invasive, objective and quantitative way. Methods and ResultsNinety bunches of nine different red cultivars of Vitis viniferaL. were photographed with a colour camera, and their bunch compactness was determined by visual inspection. A predictive partial least squares (PLS) model was developed in order to estimate bunch compactness from the morphological features extracted by automated image analysis, after the supervised segmentation of the images. The PLS model showed a capability of 85.3% for predicting correctly the rating of bunch compactness. The most discriminant variables of the model were highly correlated with the tightness of the berries in the bunch (proportion of visibility of berries, rachis and holes) and with the shape of the bunch (roundness, compactness shape factor and aspect ratio). ConclusionsThe non-invasive, image analysis methodology presented here enables the quantitative assessment of bunch compactness, thereby providing precise objective information for this key parameter. Significance of the StudyA quantitative, objective and accurate system based on image analysis was developed as an alternative to current visual methods for the estimation of bunch compactness. This novel method could be applied to the classification of table grapes and/or at the receival point of wineries for sorting and assessment of wine grapes before vinification.
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    Assessment of cluster yield components by image analysis
    (John Wiley & Sons, 2015-04) Diago, Maria P.; Tardaguila, Javier; Aleixos Borrás, María Nuria; Millan, Borja; Prats Montalbán, José Manuel; Cubero, Sergio; BLASCO IVARS, JOSE; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Dpto. de Ingeniería Gráfica; Escuela Técnica Superior de Ingeniería Industrial; Grupo de Ingeniería Estadística Multivariante GIEM; Universitat Politècnica de València; Ministerio de Economía y Competitividad; Instituto Nacional de Investigaciones Agrarias; Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria; Ministerio de Ciencia e Innovación
    [EN] BACKGROUND: Berry weight, berry number and cluster weight are key parameters for yield estimation for wine and tablegrape industry. Current yield prediction methods are destructive, labour-demanding and time-consuming. In this work, a new methodology, based on image analysis was developed to determine cluster yield components in a fast and inexpensive way. RESULTS: Clusters of seven different red varieties of grapevine (Vitis vinifera L.) were photographed under laboratory conditions and their cluster yield components manually determined after image acquisition. Two algorithms based on the Canny and the logarithmic image processing approaches were tested to find the contours of the berries in the images prior to berry detection performed bymeans of the Hough Transform. Results were obtained in two ways: by analysing either a single image of the cluster or using four images per cluster from different orientations. The best results (R2 between 69% and 95% in berry detection and between 65% and 97% in cluster weight estimation) were achieved using four images and the Canny algorithm. The model¿s capability based on image analysis to predict berry weight was 84%. CONCLUSION: The new and low-cost methodology presented here enabled the assessment of cluster yield components, saving time and providing inexpensive information in comparison with current manual methods. © 2014 Society of Chemical Industry
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    Application for the estimation of the standard citrus colour index (CCI) using image processing in mobile devices
    (Elsevier, 2018) Cubero-García, Sergio; Albert Gil, Francisco Eugenio; Prats Montalbán, José Manuel; Fernandez-Pacheco, Daniel G.; Blasco Ivars, José; Aleixos Borrás, María Nuria; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Dpto. de Ingeniería Gráfica; Escuela Técnica Superior de Ingeniería Industrial; Grupo de Ingeniería Estadística Multivariante GIEM; Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria; European Regional Development Fund; Ministerio de Economía y Competitividad
    [EN] The collection of oranges normally begins before they have reached the typical orange colour. Moreover, citrus fruits are subjected to certain degreening treatments that depend on the standard citrus colour index (CCI) at harvest. In order to facilitate the measure of this index, a free application that uses image processing techniques has been developed for Android-based mobile devices using the built-in camera of the device. The image analysis process is performed on all the images from the live input of the camera to obtain the CCI of such fruit using the open source OpenCV library. For this purpose, the RGB (red, green and blue colour coordinates) average value of a pre-selected area of the input image is calculated and then converted to HunterLab colour space to finally calculate the CCI. Several tests were carried out in the field with the fruit on the trees and under laboratory conditions with different varieties of oranges (Navel, Bonanza, Cram and Navelina) at different stages of maturity, and using different Android devices. The results were obtained for each device and condition in relation to the colour measured by a camera and compared with the performance of a panel of workers who evaluated the colour using the traditional methods. Best R-2 values obtained were 0.854 for outdoors conditions and 0.881 when measurements were done indoors.