Machine vision based quality evaluation of Iyokan orange fruit using neural networks

Kondo, Naoshi; Ahmad, Usman; Monta, Mitsuji; Murase, Haruhiko

doi:10.1016/s0168-1699(00)00141-1

Cited by 93 publications

(46 citation statements)

References 4 publications

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“…Also in tomatoes, Jahns et al (2001) found that the dominating wavelength calculated from RGB images increases for increasing maturity, and that a significant negative correlation was found between firmness stage (elasticity modulus) and such dominant wavelength. Sugar content of Iyokan orange was estimated (R 2 = 0.6) by means of neural networks by making use of colour, shape and roughness extracted from RGB images by Kondo et al (2000).…”

Section: Introductionmentioning

confidence: 99%

Multispectral images of peach related to firmness and maturity at harvest

Lleó

Elorza

Ruiz-Altisent

et al. 2009

Journal of Food Engineering

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Keywords:Fruit Multispectral Vision Ripeness Firmness Maturity Non-destructive Quality Visible spectra Chlorophyll Peach Two multispectral maturity classifications for red soft-flesh peaches ('Kingcrest', 'Rubyrich' and 'Richlady' n = 260) are proposed and compared based on R (red) and R/IR (red divided by infrared) images obtained with a three CCD camera (800 nm, 675 nm and 450 nm). R/IR histograms were able to correct the effect of 3D shape on light reflectance and thus more Gaussian histograms were produced than R images. As fruits ripened, the R/IR histograms showed increasing levels of intensity. Reference measurements such as firmness and visible spectra also varied significantly as the fruit ripens, firmness decreased while reflectance at 680 nm increased (chlorophyll absorption peak).

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Section: Introductionmentioning

confidence: 99%

Multispectral images of peach related to firmness and maturity at harvest

Lleó

Elorza

Ruiz-Altisent

et al. 2009

Journal of Food Engineering

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“…It was found that these techniques were able to determine the presence or absence of a stem with certainty, however, stem location was correctly estimated in 93, 90 and 98% for the different techniques, respectively, in the samples tested. The work of Kondo [17] studied the relation of appearance with sweetness of oranges using image processing and had a positive result about it was positive, the method could successfully predict the sweetness by the orange format.…”

Section: Image Processing Analysis On Orangesmentioning

confidence: 99%

Image Processing on Orange Industry, a Brief Review

Fermo¹,

Andrade²

2018

dtetr

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“…El crecimiento en la aplicación de las RNA es tal, que ya no solo se busca presentar-las como una alternativa de clasificación, sino, además, como un modelo capaz de evaluar sustancialmente más características a partir de un conjunto más cerrado; por ejemplo, Kondo et al (24) hacen uso de redes neuronales artificiales para predecir el contenido de azúcar de la naranja a partir de la apariencia, y así clasificarla según su calidad.…”

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Identificación del estado de madurez de las frutas con redes neuronales artificiales, una revisión

Figueredo-Ávila

Ricaurte

2016

Cien. Agri.

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ResumenLa aplicación de las Redes Neuronales Artificiales (RNA) y de la visión artificial tiene cada vez más acogida en la industria de productos alimenticios; estas técnicas priorizan la clasificación, el reconocimiento de patrones y la predicción de las cosechas y de los cambios físicos de sus productos. En este artículo se define el concepto de red neuronal y se describen sus principales características y modelos, y, por otro lado, se define el concepto de procesamiento de imágenes digitales y las diferentes etapas que lo componen. Complementariamente, se exponen las generalidades de la inspección de frutas (enfocada en Colombia) y sus técnicas. Finalmente, se especifican diferentes trabajos en los que se aplicaron técnicas de RNA y visión artificial en el campo de los productos alimenticios, dispuestos por áreas de aplicación, y se identifica de manera concluyente el impacto que estas dos técnicas tienen para la clasificación, el reconocimiento de patrones y la predicción en el sector de productos alimenticios.Palabras clave: inspección de alimentos; procesamiento de imágenes; reconocimiento de objetos; Redes Neuronales Artificiales (RNA). AbstractThe application of Artificial Neural Networks (ANNs) and artificial vision has received more and more acceptance in the food industry. These techniques prioritize the classification, pattern recognition, and prediction of the harvests and physical changes in the products. In order to understand the impact of these techniques, this article defines the concept of neural network and describes its main characteristics and models; and, on the other hand, defines the concept of digital imagery processing and its different stages. Complementarily, this review presents an overview of fruit inspection (focused on Colombia) and its techniques; and specifies and orders by application area different works in which ANNs techniques

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Machine vision based quality evaluation of Iyokan orange fruit using neural networks

Cited by 93 publications

References 4 publications

Multispectral images of peach related to firmness and maturity at harvest

Multispectral images of peach related to firmness and maturity at harvest

Image Processing on Orange Industry, a Brief Review

Identificación del estado de madurez de las frutas con redes neuronales artificiales, una revisión

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