Estimation of bodyweight from body measurements and determination of body measurements on Limousin cattle using digital image analysis

Özkaya, Serkan; Neja, W.; Krężel-Czopek, Sylwia; Oler, Adam

doi:10.1071/an14943

Cited by 14 publications

(10 citation statements)

References 7 publications

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“…The performance of the stepwise linear regression for LW was much poorer than the ANN (R 2 = 0.54, RMSE = 51). Ozkaya et al (2016) used multiple linear regression of measurements extracted from lateral 2D digital images of Limousin cattle to predict LW with an R 2 of 0.89. Although sex and breed type had low importance (3 and 0, respectively, Table 3), to investigate the performance of sex and breed specific models the ANN was trained only using the Aberdeen Angus steers data subset (n = 441, Table 1).…”

Section: Prediction Of Liveweight Cold Carcass Weight and Saleable Meat Yieldmentioning

confidence: 99%

“…As imaging technologies become more advanced and affordable it is now economically feasible to implement them on commercial farms. Ozkaya et al (2016) demonstrated that body measurements of cattle (body length, wither height, chest depth. and hip height) can be accurately determined from 2-dimensional (2D) digital image analysis (90-98% accuracy).…”

Section: Introductionmentioning

confidence: 99%

“…and hip height) can be accurately determined from 2-dimensional (2D) digital image analysis (90-98% accuracy). Applications for 2D imaging have included estimating liveweight (LW) of broiler chickens (Mollah et al, 2010), pigs (Kashiha et al, 2014;Wongsriworaphon et al, 2015;Shi et al, 2016) and beef cattle (Ozkaya et al, 2016), and LW (Tasdemir et al, 2011), body condition score (Bewley et al, 2008), and lameness (Viazzi et al, 2014) in dairy cows.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using 3D Imaging and Machine Learning to Predict Liveweight and Carcass Characteristics of Live Finishing Beef Cattle

Miller

Hyslop

Barclay

et al. 2019

Front. Sustain. Food Syst.

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Section: Prediction Of Liveweight Cold Carcass Weight and Saleable Meat Yieldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using 3D Imaging and Machine Learning to Predict Liveweight and Carcass Characteristics of Live Finishing Beef Cattle

Miller

Hyslop

Barclay

et al. 2019

Front. Sustain. Food Syst.

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“…Measurement methods of livestock morphometric data were treated according to Ozkaya et al (2016). The BW data were obtained by weighing the animal on a 0.5 kilograms scale (Iconix FX-1, New Zealand) with a perpendicular position to the surface.…”

Section: Measurement Methodsmentioning

confidence: 99%

Body Weight Estimation of Bali Cattle in Banyumulek Techno Park, West Nusa Tenggara using Several Morphometric Parameters

et al. 2018

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This study was conducted to find the best linear regression equation to estimate the body weight (BW) of female Bali cattle in Banyumulek Techno Park, West Nusa Tenggara based on three morphometric parameters, i.e. withers height (WH), body length (BL), and heart girth (HG). Data collection was conducted by random sampling of female Bali cattle (1-5 years of age) in Banyumulek Techno Park. A total of 63 female Bali cattle were used in this study. The parameter of WH (distance from the ground level to the highest point of withers) and BL (distance from the shoulder joint (later tuberosity of humerus) to the pelvic bone) were measured with a measuring stick. The HG data (circumference around the chest at the fourth rib) was measured with a measuring tape. Identification of age was carried out based on the animal recording book and incisors condition. The descriptive statistic analysis (mean value, standard deviation, the coefficient of variation, minimum and maximum value) of the morphometric and BW data were performed using MS Excel 2017 program. The linear regression equation analysis and the comparison between actual and estimated BW values (Independent sample T-test) were performed using SPSS 16.0 program. The BL and BW parameters had the highest correlation coefficient (r) value (0.86). The test result showed that the predicted BW value of female Bali cattle was not significantly different with the actual BW when using G model of linear regression equation (three variables used). The G model of linear regression equation had the highest R2 value (0.84). However the R2 value in the F model with two variables (BL and HG) was also high (0.83). It can be concluded that the simple linear regression equation BW=(0.96)(WH)+(1.26)(BL)+(1.57)(HG)-284.92 are the best model to estimate the BW value of female Bali cattle in Banyumulek Techno Park.

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“…Body measurements in the animal studied consisted of body length (BL), withers height (WH) and heart girth (HG) as presented in Figure 3. According to Ozkaya et al (2016), data of BL was obtained by measuring the distance between the shoulder joint (later tuberosity of humerus) to the edge of the pelvic bone. Data of WH was obtained by measuring the distance from the withers to the surface by the perpendicular line.…”

Section: Body Measurements Body Weight and Ages Identificationmentioning

confidence: 99%

Polymorphism of Bovine Growth Hormone Receptor Gene (g.3338A>G) and Its Association with Body Measurements and Body Weight in Pasundan Cows

Putra¹,

Agung²,

Anwar³

et al. 2019

Trop. Anim. Sci. J.

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Bovine growth hormone receptor (bGHR) gene was one of the growth hormone family genes that important for body growth and development of cattle. This research was carried out to identify the polymorphism in the exon 10 of bGHR gene from 142 heads of Pasundan cows at West Java and its association with body length (BL), withers height (WH), heart girth (HG), and body weight (BW). There are two mutation points in the bGHR gene based on the sequencing analysis i.e. g.3322del.A and g.3338A>G. The single nucleotide polymorphism (SNP) at g.3338A>G was detected using PCR-RFLP method with AluI restriction enzyme and showed three genotypes of AA (0.49), AG (0.37), and GG (0.14). The allelic frequencies were 0.67 (A) and 0.33 (G). The number of allele effective (ne) value was 1.79 and described that A allele was the higher allele in the bGHR gene in this study. The polymorphic informative content (PIC) value was 0.34 and categorized as a moderate category. The Chi-square (χ2) analysis showed that the population sample was in the Hardy-Weinberg equilibrium (χ2<5.99). It was concluded that the polymorphism of bGHR gene had a significant association (p<0.05) with BL, WH, and BW of Pasundan cows. Research showed that all body measurements in GG genotype animals were lowest than other genotypes. Meanwhile, the highest of BW was showed in GG genotype. In addition, the average of BL, WH, HG, and BW in Pasundan cows (2 PPI and 3PPI) were 126.88±14.25 cm; 133.97±31.69 cm; 145.35±13.56 cm and 201.85±44.87 kg, respectively.

show abstract

Estimation of bodyweight from body measurements and determination of body measurements on Limousin cattle using digital image analysis

Cited by 14 publications

References 7 publications

Using 3D Imaging and Machine Learning to Predict Liveweight and Carcass Characteristics of Live Finishing Beef Cattle

Using 3D Imaging and Machine Learning to Predict Liveweight and Carcass Characteristics of Live Finishing Beef Cattle

Body Weight Estimation of Bali Cattle in Banyumulek Techno Park, West Nusa Tenggara using Several Morphometric Parameters

Polymorphism of Bovine Growth Hormone Receptor Gene (g.3338A>G) and Its Association with Body Measurements and Body Weight in Pasundan Cows

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