Ripeness estimation of grape berries and seeds by image analysis

Rodríguez-Pulido, Francisco J.; Gómez‐Robledo, Luis; Melgosa, Manuel; Gordillo, Belén

doi:10.1016/j.compag.2012.01.004

Cited by 63 publications

(52 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Granitto et al 2002, Mebatsion et al 2013, fruits (e.g. Kondo et al 2000, Kurtulmus et al 2011, Mollazade et al 2012, Rodríguez-Pulido et al 2012, Payne et al 2013, leaves (e.g. Buschmann andNagel 1993, Buschmann et al 2012) or entire plants or vegetation (e.g.…”

Section: ---mentioning

confidence: 99%

Measurements of reflectance and fluorescence spectra for nondestructive characterizing ripeness of grapevine berries

Navrátil¹,

Buschmann²

2016

Photosynt.

View full text Add to dashboard Cite

In vivo reflectance and fluorescence spectra from berry skins of a white (Riesling) and red (Cabernet Sauvignon) grapevine variety were measured during a ripening season with a new CMOS radiometer instrument. Classical reference measurements were also carried out: sugar content of the berry juice [°Brix], pigment content (chlorophyll a and b, carotenoids, anthocyanins) from methanol extracts of the berry skin. We showed that the spectra and the colors analyzed from them could be taken as an unambiguous indicator of grapevine ripening. Reflectance spectra, which were affected by the content of pigments (chlorophylls and anthocyanins), effects of surface (wax layers), and tissue structure (cell size) of the berries well correlated (R 2 = 0.89) with the °Brix measurements of the berries. The fast data acquisition of both reflectance and fluorescence spectra in one sample with our radiometer instrument made it superior over the timeconsuming, traditional, and mostly destructive chemical analysis used in vine growing management.

show abstract

Section: ---mentioning

confidence: 99%

Measurements of reflectance and fluorescence spectra for nondestructive characterizing ripeness of grapevine berries

Navrátil¹,

Buschmann²

2016

Photosynt.

View full text Add to dashboard Cite

show abstract

“…Se consideraron semillas de uva de las cosechas de 2009 y 2010. El algoritmo programado permitió la medida del color en el espacio CIELAB, y en cuanto a apariencia, se midió en cada semilla el largo, ancho, relación de aspecto, grado de redondez, perímetro, área y heterogeneidad, definiéndose ésta como la fracción de píxeles cuya intensidad difiere más de un 10 % de la media, como se describe en Rodríguez-Pulido et al [19].…”

Section: Resultados Y Discusiónunclassified

Application of imaging techniques for the evaluation of phenolic maturity of grape seeds

Rodríguez-Pulido¹,

González-Miret²,

Heredia³

2017

Opt. Pura Apl.

View full text Add to dashboard Cite

ABSTRACT:Quality of wine depends fundamentally on the condition of the components of grapes at the moment of harvest. In this respect, their seeds directly affect to structure, astringency, stability, and indirectly to colour of wine. In this work, the grape seeds have been evaluated by techniques of image analysis. In all cases, the methodology was developed trying to entail the minimum sample preparation and maximum quickness of the method, and as far as possible, substitute the chemical analysis that usually involves long and tedious processes that hamper monitoring of ripening in a rapid way. The proposed methodology for measuring and evaluating colour and near infrared spectrum has yielded satisfactory results for estimating the ripening of grape seeds, and it has been useful for establishing stages of ripeness from their optical features.Key words: Colorimetry, Image Analysis, Grape seeds, Hyperspectral Imaging. RESUMEN:La calidad del vino depende en gran medida del estado en que se encuentren los diferentes componentes de la uva en el momento de la recolección y, en este sentido, las semillas afectan directamente a su astringencia, así como a la estructura y estabilidad del color del vino. En el presente trabajo se han evaluado semillas de uva de diferentes variedades aplicando distintas técnicas de análisis de imagen. En todos los casos se han desarrollado metodologías que implican la mínima preparación de muestra y la máxima rapidez del método, con el objeto de poder sustituir, en la medida de la posible, el análisis químico, que suele suponer largos y tediosos procesos que impiden un seguimiento rápido de la maduración. La metodología propuesta para la medición y evaluación del color y el espectro infrarrojo cercano ha dado resultados satisfactorios en la estimación del estado de madurez de las semillas de uva, y ha sido útil para establecer etapas de maduración a partir de sus propiedades ópticas.Palabras clave: Colorimetría, Análisis de Imagen, Semillas de uva, Análisis de imágenes hiperespectrales. REFERENCIAS Y ENLACES[1] H. R. Schultz and G. V. Jones, "Climate Induced Historic and Future Changes in Viticulture," J Wine Res 21, 137-145 (2010).

show abstract

“…Basic machine vision components are: (a) image sensor (charge-couple device (CCD) or complementary metal oxide semiconductor (CMOS)), (b) illuminant (D-65, fluorescent lamps, among others), (c) the background (with high contrast for the object of interest), (d) trigger device (used to start image acquisition), and (e) a frame grabber (to capture the actual image). The capture procedure can be divided into the following steps: the illuminator irradiates the object, the image sensor receives the light reflected from the object and the background, and when the trigger is activated, the system extracts the color characteristics and converts them to electrical signals and then the grabber frame stores the image taken [22,23]. Once the image is on the computer or other processing device, image processing algorithms are used to analyze the data.…”

Section: Introductionmentioning

confidence: 99%

Color Analysis and Image Processing Applied in Agriculture

Sandoval¹,

Rosas²,

Sandoval³

et al. 2018

Automation in Agriculture - Securing Food Supplies for Future Generations

View full text Add to dashboard Cite

Color and appearance are perhaps the first attributes that attract us to a fruit or vegetable. Since the appearance of the product generally determines whether a product is accepted or rejected, measuring the color characteristics becomes an important task. To carry out the analysis of this key attribute for agriculture, it is recommended to use an artificial vision system to capture the images of the samples and then to process them by applying colorimetric routines to extract color parameters in an efficient and nondestructive manner, which makes it a suitable tool for a wide range of applications. The purpose of this chapter is to give an overview on recent development of image processing applied to color analysis from horticultural products, more specifically the practical usage of color image analysis in agriculture. As an example, quantitative values of color are extracted from Habanero Chili Peppers using image processing; the images from the samples were obtained using a desktop configuration of machine vision system. The material presented should be useful for students starting on the field, as well as for researchers looking for state-of-the-art studies and practical applications.

show abstract

Ripeness estimation of grape berries and seeds by image analysis

Cited by 63 publications

References 9 publications

Measurements of reflectance and fluorescence spectra for nondestructive characterizing ripeness of grapevine berries

Measurements of reflectance and fluorescence spectra for nondestructive characterizing ripeness of grapevine berries

Application of imaging techniques for the evaluation of phenolic maturity of grape seeds

Color Analysis and Image Processing Applied in Agriculture

Contact Info

Product

Resources

About