Application of computer vision systems for estimation of fat content in poultry meat

Chmiel, Marta; Słowiński, Mirosław; Dasiewicz, K.

doi:10.1016/j.foodcont.2011.03.002

Cited by 36 publications

(21 citation statements)

References 14 publications

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“…Machine vision systems have been widely used in agriculture and food for detection of surface quality characteristics (i.e., color, size, shape, etc.) (Chmiel et al, 2011a(Chmiel et al, , 2011bPenman, 2001;Zhang et al, 2015).…”

Section: Main Optical Technologiesmentioning

confidence: 99%

“…Machine vision and image analysis have been used for detection of fat content in chicken and turkey (Chmiel et al, 2011a), to estimate live weight of broilers for monitoring growth (Mollah et al, 2010), and for inspection of poultry carcasses for wholesomeness (Chao et al, 2000(Chao et al, , 2007. Table 1 summarizes research studies that have been reported since 2003 on hyperspectral imaging, Vis/NIR spectroscopy, and machine vision methods for quality detection of meat and meat products.…”

Section: Other Meatsmentioning

confidence: 99%

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Optical Methods and Techniques for Meat Quality Inspection

Peng¹,

Dhakal²

2015

Trans.ASABE

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Section: Main Optical Technologiesmentioning

confidence: 99%

Section: Other Meatsmentioning

confidence: 99%

Optical Methods and Techniques for Meat Quality Inspection

Peng¹,

Dhakal²

2015

Trans.ASABE

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“…Based on image analysis, Font-i-Furnols et al (2015) and Carabús et al (2015a,b) analysed serial slaughter data from 30 to 120 kg for gilts of different genotypes and pigs of different sexes, respectively, and obtained et al, 1983) and it is mainly used on line to estimate carcass characteristics. Nevertheless, it can also be used (not always on line) to predict the intramuscular fat or colour in chicken and turkey, with a R 2 =0.22-0.74 (Chmiel et al, 2011), tenderness in beef (R 2 =0.12-0.70; Li et al, 1999;Sun et al, 2012) and lean meat yield in live pigs (Doeschl Wilson et al, 2005). Published results have demonstrated that VIA can measure the growth rate and weight of pigs (Schoefield, 1990;White et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Imaging technologies to study the composition of live pigs: A review

Carabús¹,

Gispert²,

Font-i-Furnols³

2016

Span. j. agric. res.

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Image techniques are increasingly being applied to livestock animals. This paper overviews recent advances in image processing analysis for live pigs, including ultrasound, visual image analysis by monitoring, dual-energy X-ray absorptiometry, magnetic resonance imaging and computed tomography. The methodology for live pigs evaluation, advantages and disadvantages of different devices, the variables and measurements analysed, the predictions obtained using these measurements and their accuracy are discussed in the present paper. Utilities of these technologies for livestock purposes are also reviewed. Computed tomography and magnetic resonance imaging yield useful results for the estimation of the amount of fat and lean mass either in live pigs or in carcasses. Ultrasound is not sufficiently accurate when high precision in estimating pig body composition is necessary but can provide useful information in agriculture to classify pigs for breeding purposes or before slaughter. Improvements in factors, such as the speed of scanning, cost and image accuracy and processing, would advance the application of image processing technologies in livestock animals.

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“…La visión artificial es una tecnología sofisticada de inspección de uso común en diversas industrias, por ejemplo en la acuacultura (Zion, 2012), la horticultura (Moreda, 2012) y es aplicable en la determinación de la cantidad de grasa en aves de corral (Chmiel, 2011). La visión artificial la componen un conjunto de procesos destinados a realizar el análisis de imágenes.…”

Section: Introductionunclassified

Determinación por Visión Artificial del Factor de Degradación en Aleaciones Biocompatibles

2013

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ResumenSe determinó por visión artificial el factor de degradación de una aleación biocompatible, AISI 316LVM. Para ello, se utilizó una solución fisiológica simulada (solución de Hanks), electrolito que simula la composición presente en el organismo, es decir, el ambiente donde el implante se utilizará. El comportamiento electroquímico fue evaluado mediante curvas potencio-dinámicas. La caracterización superficial se desarrolló mediante un estereoscopio y los productos de corrosión se evaluaron mediante difracción de rayos X. El sistema usó una imagen microscópica de la superficie del material en su estado natural (brillo espejo) como parámetro base para la comparación, para definir en qué estado se encuentran las muestras una vez han pasado por las pruebas realizadas. Se encontró, que es posible estimar el factor de degradación o de deterioro en un material mediante un análisis topográfico del mismo. Palabras clave: biomateriales, degradación, visión artificial, 316LVM Computer Vision for Determination of Degradation Factor in Biocompatible Alloys AbstractThe degradation factor of a biocompatible alloy, AISI 316LVM, was determined by computer vision. For this, a simulated physiological solution (Hanks' solution) which simulates the electrolyte composition in the body, that is the environment in which the implant is used. The electrochemical behavior was evaluated by potentio-dynamic curves. The surface characterization was performed using a stereoscope and corrosion products were evaluated by X-ray diffraction. The system used a microscopic image of the surface of the material in its natural state (mirror finish) as a basis for comparison parameter to define what state are once samples have undergone testing. It was found that it is possible to estimate the factor of degradation or deterioration of a material through a topographic analysis.

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Application of computer vision systems for estimation of fat content in poultry meat

Cited by 36 publications

References 14 publications

Optical Methods and Techniques for Meat Quality Inspection

Optical Methods and Techniques for Meat Quality Inspection

Imaging technologies to study the composition of live pigs: A review

Determinación por Visión Artificial del Factor de Degradación en Aleaciones Biocompatibles

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