Relationship between pig carcass characteristics measured in live pigs or carcasses with Piglog, Fat-o-Meat’er and computed tomography

Lucas, Daniel; Brun, A.; Gispert, M.; Carabús, Anna; Soler, J.; Tibau, J.; Font-i-Furnols, M.

doi:10.1016/j.livsci.2017.01.010

Cited by 16 publications

(12 citation statements)

References 15 publications

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“…These techniques include the Fat-O-Meat’er (FOM), the UltraFOM (UFOM), the AutoFOM, and a vision-based video image analyzer (VCS2000) ( Font I Furnols and Gispert, 2009 ). FOM is a handheld optical probe that measure backfat thickness and loin muscle depth in predefine location on the carcass to predict the lean meat content ( Lucas et al, 2017 ). However, equipment such as UltraFOM and AutoFOM use reflectance of ultrasound to measure the lean meat percentage and fat thickness.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Estimation of Lean Meat Yield of South Korean Pig Carcasses Using Machine Vision Technique

Lohumi¹,

Wakholi²,

Baek³

et al. 2018

Korean J Food Sci Anim Resour

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In this paper, we report the development of a nondestructive prediction model for lean meat percentage (LMP) in Korean pig carcasses and in the major cuts using a machine vision technique. A popular vision system in the meat industry, the VCS2000 was installed in a modern Korean slaughterhouse, and the images of half carcasses were captured using three cameras from 175 selected pork carcasses (86 castrated males and 89 females). The imaged carcasses were divided into calibration (n=135) and validation (n=39) sets and a multilinear regression (MLR) analysis was utilized to develop the prediction equation from the calibration set. The efficiency of the prediction equation was then evaluated by an independent validation set. We found that the prediction equation—developed to estimate LMP in whole carcasses based on six variables—was characterized by a coefficient of determination (Rv2) value of 0.77 (root-mean square error [RMSEV] of 2.12%). In addition, the predicted LMP values for the major cuts: ham, belly, and shoulder exhibited Rv2 values≥0.8 (0.73 for loin parts) with low RMSEV values. However, lower accuracy (Rv(2)=0.67) was achieved for tenderloin cuts. These results indicate that the LMP in Korean pig carcasses and major cuts can be predicted successfully using the VCS2000-based prediction equation developed here. The ultimate advantages of this technique are compatibility and speed, as the VCS2000 imaging system can be installed in any slaughterhouse with minor modifications to facilitate the on-line and real-time prediction of LMP in pig carcasses.

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Section: Introductionmentioning

confidence: 99%

Nondestructive Estimation of Lean Meat Yield of South Korean Pig Carcasses Using Machine Vision Technique

Lohumi¹,

Wakholi²,

Baek³

et al. 2018

Korean J Food Sci Anim Resour

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show abstract

“…Several medical diagnostic imaging technologies have also been employed for many applications with animals such as evaluation of muscle and fat composition, and bone mineralization in live animals and carcasses (37)(38)(39). Some worth noting technologies include ultrasound (US), dual-energy xray absorptiometry (DXA), computed tomography (CT), and magnetic resonance (MR).…”

Section: Other Imaging Technologies Of Importancementioning

confidence: 99%

Image Analysis and Computer Vision Applications in Animal Sciences: An Overview

2020

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Computer Vision, Digital Image Processing, and Digital Image Analysis can be viewed as an amalgam of terms that very often are used to describe similar processes. Most of this confusion arises because these are interconnected fields that emerged with the development of digital image acquisition. Thus, there is a need to understand the connection between these fields, how a digital image is formed, and the differences regarding the many sensors available, each best suited for different applications. From the advent of the charge-coupled devices demarking the birth of digital imaging, the field has advanced quite fast. Sensors have evolved from grayscale to color with increasingly higher resolution and better performance. Also, many other sensors have appeared, such as infrared cameras, stereo imaging, time of flight sensors, satellite, and hyperspectral imaging. There are also images generated by other signals, such as sound (ultrasound scanners and sonars) and radiation (standard x-ray and computed tomography), which are widely used to produce medical images. In animal and veterinary sciences, these sensors have been used in many applications, mostly under experimental conditions and with just some applications yet developed on commercial farms. Such applications can range from the assessment of beef cuts composition to live animal identification, tracking, behavior monitoring, and measurement of phenotypes of interest, such as body weight, condition score, and gait. Computer vision systems (CVS) have the potential to be used in precision livestock farming and high-throughput phenotyping applications. We believe that the constant measurement of traits through CVS can reduce management costs and optimize decision-making in livestock operations, in addition to opening new possibilities in selective breeding. Applications of CSV are currently a growing research area and there are already commercial products available. However, there are still challenges that demand research for the successful development of autonomous solutions capable of delivering critical information. This review intends to present significant developments that have been made in CVS applications in animal and veterinary sciences and to highlight areas in which further research is still needed before full deployment of CVS in breeding programs and commercial farms.

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“…FOM is a handheld optical probe that measure backfat thickness and loin muscle depth in predefine location on the carcass to predict the lean meat content (Lucas et al, 2017). However, equipment such as UltraFOM and AutoFOM use reflectance of ultrasound to measure the lean meat percentage and fat thickness.…”

Section: Introductionmentioning

confidence: 99%

Erratum to: Nondestructive Estimation of Lean Meat Yield of South Korean Pig Carcasses Using Machine Vision Technique

Lohumi¹,

Wakholi²,

Baek³

et al. 2019

Food Sci Anim Resour

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Relationship between pig carcass characteristics measured in live pigs or carcasses with Piglog, Fat-o-Meat’er and computed tomography

Cited by 16 publications

References 15 publications

Nondestructive Estimation of Lean Meat Yield of South Korean Pig Carcasses Using Machine Vision Technique

Nondestructive Estimation of Lean Meat Yield of South Korean Pig Carcasses Using Machine Vision Technique

Image Analysis and Computer Vision Applications in Animal Sciences: An Overview

Erratum to: Nondestructive Estimation of Lean Meat Yield of South Korean Pig Carcasses Using Machine Vision Technique

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