Objective on-line assessment of marbling: a brief review

2004 ◽  
Vol 44 (7) ◽  
pp. 681 ◽  
Author(s):  
D. M. Ferguson
Keyword(s):  
Image Analysis ◽  
Measurement Accuracy ◽  
Subjective Evaluation ◽  
Bioelectrical Impedance ◽  
Visual Assessment ◽  
Objective Measurement ◽  
Video Image ◽  
Line Measurement ◽  
On Line ◽  
Visual Assessments

Visual assessment remains the preferred method for evaluation of marbling in abattoirs. However, visual assessments are compromised by the disadvantages inherent in most systems of subjective evaluation. To that end, several objective measurement technologies have been developed and evaluated for on-line measurement of marbling. Several of these technologies are reviewed in this paper. Of the current suite of technologies, video image analysis and the Danish bioelectrical impedance device offer the most promise in terms of measurement accuracy and suitability for on-line use.

scholarly journals Estimation of Secondary Soil Properties by Fusion of Laboratory and On-Line Measured Vis–NIR Spectra

2019 ◽  
Vol 11 (23) ◽  
pp. 2819 ◽  
Author(s):  
Muhammad Abdul Munnaf ◽  
Said Nawar ◽  
Abdul Mounem Mouazen
Keyword(s):  
Soil Properties ◽  
Real Time ◽  
Measurement Accuracy ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Spectral Response ◽  
Nir Spectroscopy ◽  
Accurate Estimation ◽  
Line Measurement ◽  
On Line

Visible and near infrared (vis–NIR) diffuse reflectance spectroscopy has made invaluable contributions to the accurate estimation of soil properties having direct and indirect spectral responses in NIR spectroscopy with measurements made in laboratory, in situ or using on-line (while the sensor is moving) platforms. Measurement accuracies vary with measurement type, for example, accuracy is higher for laboratory than on-line modes. On-line measurement accuracy deteriorates further for secondary (having indirect spectral response) soil properties. Therefore, the aim of this study is to improve on-line measurement accuracy of secondary properties by fusion of laboratory and on-line scanned spectra. Six arable fields were scanned using an on-line sensing platform coupled with a vis–NIR spectrophotometer (CompactSpec by Tec5 Technology for spectroscopy, Germany), with a spectral range of 305–1700 nm. A total of 138 soil samples were collected and used to develop five calibration models: (i) standard, using 100 laboratory scanned samples; (ii) hybrid-1, using 75 laboratory and 25 on-line samples; (iii) hybrid-2, using 50 laboratory and 50 on-line samples; (iv) hybrid-3, using 25 laboratory and 75 on-line samples, and (v) real-time using 100 on-line samples. Partial least squares regression (PLSR) models were developed for soil pH, available potassium (K), magnesium (Mg), calcium (Ca), and sodium (Na) and quality of models were validated using an independent prediction dataset (38 samples). Validation results showed that the standard models with laboratory scanned spectra provided poor to moderate accuracy for on-line prediction, and the hybrid-3 and real-time models provided the best prediction results, although hybrid-2 model with 50% on-line spectra provided equally good results for all properties except for pH and Na. These results suggest that either the real-time model with exclusively on-line spectra or the hybrid model with fusion up to 50% (except for pH and Na) and 75% on-line scanned spectra allows significant improvement of on-line prediction accuracy for secondary soil properties using vis–NIR spectroscopy.

scholarly journals Application of Image Analysis in Studies of Quantitative Disease Resistance, Exemplified Using Common Bacterial Blight–Common Bean Pathosystem

2012 ◽  
Vol 102 (4) ◽  
pp. 434-442 ◽  
Author(s):  
Weilong Xie ◽  
Kangfu Yu ◽  
K. Peter Pauls ◽  
Alireza Navabi
Keyword(s):  
Image Analysis ◽  
Common Bean ◽  
Disease Severity ◽  
Bacterial Blight ◽  
Visual Assessment ◽  
Common Bacterial Blight ◽  
Genetic Studies ◽  
Visual Scale ◽  
Quantitative Genetic ◽  
Visual Assessments

The effectiveness of image analysis (IA) compared with an ordinal visual scale, for quantitative measurement of disease severity, its application in quantitative genetic studies, and its effect on the estimates of genetic parameters were investigated. Studies were performed using eight backcross-derived families of common bean (Phaseolus vulgaris) (n = 172) segregating for the molecular marker SU91, known to be associated with a quantitative trait locus (QTL) for resistance to common bacterial blight (CBB), caused by Xanthomonas campestris pv. phaseoli and X. fuscans subsp. fuscans. Even though both IA and visual assessments were highly repeatable, IA was more sensitive in detecting quantitative differences between bean genotypes. The CBB phenotypic difference between the two SU91 genotypic groups was consistently more than fivefold for IA assessments but generally only two- to threefold for visual assessments. Results suggest that the visual assessment results in overestimation of the effect of QTL in genetic studies. This may have been caused by lack of additivity and uneven intervals of the visual scale. Although visual assessment of disease severity is a useful tool for general selection in breeding programs, assessments using IA may be more suitable for phenotypic evaluations in quantitative genetic studies involving CBB resistance as well as other foliar diseases.

Video image analysis for on-line classification of lamb carcasses

1998 ◽  
Vol 67 (2) ◽  
pp. 311-316 ◽  
Author(s):  
K. Stanford ◽  
R. J. Richmond ◽  
S. D. M. Jones ◽  
W. M. Robertson ◽  
M. A. Price ◽  
...  
Keyword(s):  
Image Analysis ◽  
Classification System ◽  
Video Image ◽  
Meat Yield ◽  
Video Image Analysis ◽  
Current Classification ◽  
On Line ◽  
Waste Fat ◽  
Current Classification System

AbstractVideo image analysis (VIA), carcass shape and colour data were collected for 1211 lambs of known gender, breed type and carcass weight over a 1-week period using the VIAscan® system developed by the Australian Meat Research Corporation. Classification data (thickness of soft tissue over the 12th rib (GR measurement) and subjective conformation scores on a five-point scale of the leg, loin and shoulder) were assessed by an Agriculture and Agri-Food Canada grader after carcasses had chilled at 5°C for 3 to 6 h. Dissections into saleable meat yield (no. = 58) were performed after carcasses had chilled an additional 24 h. The timing of this study, which was dependent on availability of the VIA equipment, influenced the age and type of lambs available for analysis. The majority of lambs evaluated were wool-breed wethers, age > 10 months, of average GR (15·7 (s.d. 0·2) mm) and muscle conformation (3·0, s.d. 0·1). VIA improved the prediction of saleable meat yield (R2 = 0·71, residual s.d. = 14g/kg) compared with the current classification system (R2 = 0·52, residual s.d. = 18 g/kg). Although prediction ofGR measurement by VIA resulted in a large residual error (residual s.d. = 2·4 mm), the proportion of waste fat (perirenal and subcutaneous) and bone dissected from the carcass was accurately predicted (R2 = 0·62, residual s.d. = 11 g/kg). Proportions of leg (R2 = 0·71, residual s.d. = 7 g/kg) and shoulder (R2 = 0·62, residual s.d. = 9 g/kg) primals were also accurately predicted by VIA, although there were no significant predictors for the proportion of the loin (P > 0·15). VIA improved the prediction of saleable meat yield compared with the current classification system. However collection of additional data including some from extremely lean or well muscled animals would be required before VIA could be recommended to classify lamb carcasses

The accuracy of visual assessments for weight in young beef calves

1981 ◽  
Vol 21 (108) ◽  
pp. 19
Author(s):  
CA Morris ◽  
RJ Smith ◽  
H Hearnshaw ◽  
RW Dicker
Keyword(s):  
Selection Pressure ◽  
Visual Assessment ◽  
Objective Measurement ◽  
Beef Calves ◽  
Breeding Stock ◽  
Actual Weight ◽  
Weight Difference ◽  
Beef Bull ◽  
Selection Intensities ◽  
Visual Assessments

A trial was carried out to estimate how accurately calves sorted into classes by sex, breed and nutritional background can be placed into one of three sub-classes by visual assessment. Eight recorders independently assessed each group twice in August when calves were a month old, and six recorders repeated the exercise 4 months later. Calves were visually assessed first for liveweight, as if they were potential breeding stock ('weight assessment'), and secondly for whatever criteria the recorders might use for potential breeding stock for sale in their own herds ('stud assessment'). The objective was to simulate two possible forms of visual assessment in a bull breeder's herd, with the actual weight records being used here for comparison. Both visual assessments were compared additionally with weight records adjusted for calf age and dam age. The results were expressed in terms of the weight difference (W1) of animals visually classed by the recorder into the top vs bottom third of a group, compared with the weight difference (W2) attainable by using objective measurement at the same age, i.e. 100x W1/W2. Where the standard was unadjusted weight, then W2 in August was 31.6 kg overall. In the stud assessment recorders found that calves in the top vs bottom third differed by 17.4 kg, and by 18.4 kg in the weight assessment, equivalent respectively to 55% and 58% of the possible selection pressure attainable. Comparing only weight assessments against August weights which were adjusted for calf age and dam age, 48% of the potential selection pressure was attainable. In the December assessments, recorders were randomly allocated to first making stud assessments followed by weight assessments, or vice versa. Subsequent analysis showed that the sequence was not important. Compared with December liveweights adjusted for calf age and dam age, the selection intensities attainable by stud and weight assessment were 41 and 48%, respectively. The implications of these findings for the Australian beef bull breeding industry are discussed.

The Study of Thread Parameter Based on Machine Vision for Automatic Measurement

2013 ◽  
Vol 401-403 ◽  
pp. 1158-1161
Author(s):  
Jing Chao ◽  
Zhan Feng Liu ◽  
Yan Shu Liu
Keyword(s):  
Machine Vision ◽  
Image Recognition ◽  
Measurement Accuracy ◽  
Linear Array ◽  
Image Data ◽  
Automatic Measurement ◽  
Measuring Method ◽  
Line Measurement ◽  
On Line ◽  
Tooth Type

In order to improve the measurement accuracy of thread parameter and can realize the automatic measurement of parameter, this paper propose a non-contact measuring method which is based on machine vision, we use the industrial linear array CCD with high-precision scan the projection of the thread which in the field of parallel light, using the image recognition, image acquisition, image data processing technology enable your computer finish the pitch diameter, thread pitch, tooth type angle on a non-contact of real-time, on-line measurement. This article also provides detailed measuring method with the main parameters of thread.

On-Line Measurement of Bran in Flour by Image Analysis

Cereals ◽  
1997 ◽  
pp. 125-131 ◽  
Author(s):  
Martin B. Whitworth ◽  
Tony D. Evers ◽  
Christopher J. Brock
Keyword(s):  
Image Analysis ◽  
Line Measurement ◽  
On Line

Evaluation of Video Image Analysis (VIA) technology to predict meat yield of sheep carcasses on-line under UK abattoir conditions

Meat Science ◽  
2009 ◽  
Vol 82 (1) ◽  
pp. 94-100 ◽  
Author(s):  
E. Rius-Vilarrasa ◽  
L. Bünger ◽  
C. Maltin ◽  
K.R. Matthews ◽  
R. Roehe
Keyword(s):  
Image Analysis ◽  
Video Image ◽  
Meat Yield ◽  
Video Image Analysis ◽  
On Line

On-Line Measurement of Paper Shrinkage Using Image Analysis

1992 ◽  
Vol 25 (25) ◽  
pp. 131-135
Author(s):  
A. Guesalaga ◽  
A. Foessel ◽  
M. Guarini ◽  
A. Cipriano ◽  
H.W. Kropholler
Keyword(s):  
Image Analysis ◽  
Line Measurement ◽  
On Line
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