Publisher: CSIRO; Journal: AN:Animal Production Science

Publisher: CSIRO; Journal: AN:Animal Production Science Article Type: research-article; Volume: ; Issue: ; Article ID: AN14943 DOI: 10.1071/AN14943; TOC Head: AN14943

Estimation of bodyweight using digital image

S. Ozkaya et al.

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

Serkan OzkayaA,C, Wojciech NejaB, Sylwia Krezel-CzopekB and Adam OlerB

ASuleyman Demirel University, Agriculture Faculty, Department of Animal Science, 32260, Isparta, Turkey.

BUniversity of Technology and Life Sciences, Faculty of Animal Breeding and Biology, Department of Cattle Breeding, ul. Mazowiecka 28, 85-084 Bydgoszcz, Kujawsko-Pomorskie, Poland.

CCorresponding author. Email: [email protected]

The objective of this study was to predict bodyweight and estimate body measurements of Limousin cattle using digital image analysis (DIA). Body measurements including body length, wither height, chest depth, and hip height of cattle were determined both manually (by measurements stick) and by using DIA. Body area was determined by using DIA. The images of Limousin cattle were taken while cattle were standing in a squeeze chute by a digital camera and analysed by image analysis software to obtain body measurements of each animal. While comparing the actual and predicted body measurements, the accuracy was determined as 98% for wither height, 97% for hip height, 94% for chest depth and 90.6% for body length. Regression analysis between body area and bodyweight yielded an equation with R2 of 61.5%. The regression equation, which included all body traits, resulted in an R2 value of 88.7%. The results indicated that DIA can be used for accurate prediction of body measurements and bodyweight of Limousin cattle.

Additional keywords: body measurements, bodyweight, digital image analysis, Limousin cattle.

In recent years, computer-aided and image analysis has been used for livestock production. Therefore, the objective of this study was to determination of body measurements and estimation of bodyweight from body measurements by using digital image analysis. The results indicated that digital image analysis can be used for accurate prediction of body measurements and bodyweight. Body area can be used for estimation of bodyweight.

Introduction Bodyweight (BW) and body measurements (BM) are important economic traits in meat production cattle and are also a good indication of animal status and necessary for veterinary products management (Polgar et al. 1997; Ulutas et al. 2001). However, BM and BW of cattle are difficult to obtain because they are very time consuming, dangerous and stressful for people and animals (Tasdemir et al. 2011). Thus, most farmers do not prefer the conventional method, which is basically weighing and measuring dimensions of animals. Computer-aided and image analysis has been used for livestock production in recent years to determine meet quality, to predict BW, BM and body condition

of 8 Publisher: CSIRO; Journal: AN:Animal Production Science Article Type: research-article; Volume: ; Issue: ; Article ID: AN14943 DOI: 10.1071/AN14943; TOC Head: score (McDonald and Chen 1990; Bozkurt et al. 2007, 2008; Negretti et al. 2008). Researchers have shown that digital image analysis (DIA) has been applied to determine BM and BW of cattle (Bozkurt et al. 2007; Negretti et al. 2008; Ozkaya and Bozkurt 2008; Tasdemir et al. 2011; Ozkaya 2012).

The DIA systems can predict BW very closely (Bozkurt et al. 2007). Negretti et al. (2008) reported that computerised image analysis is an effective measurement system for the indirect determination of liveweight in Mediterranean buffalo. Ozkaya and Bozkurt (2008) showed that the prediction capability of DIA was low for prediction of BW and possibility of using body area (BA) as a parameter in predicting BW was also low. Tasdemir et al. (2011) indicated that DIA was appropriate for BW estimation of Holstein cows. Ozkaya (2012) reported that DIA provided very close agreement and reality for prediction of BM of Holstein female calves. Therefore, the objective of this study was to evaluate the result of prediction of BW from BM and BA of Limousin cattle using DIA.

Materials and methods Animals used in this study were 56 Limousin cattle reared at the Agricultural Research Centre of The Technology and Life Science University in Poland. Animals were weighed using a digital scale.

Measurements of cattle by using conventional methods Body weight and BM were taken from animals while standing in a squeeze: wither height (WH): measured at the top of the wither; body length (BL): distance from point of shoulder to ischium; chest girth (CD): from sternum area immediately caudal to the forelimb to top of the thoracic vertebra; hip height (HH): measured at top of the hip.

Digital image, image analyses and measurements of cattle by using DIA A digital camera (Canon PowerShot A720 IS, Canon Inc, NY, USA) was used to take images. The camera was set to a standard quality (8.0 mega pixels). Illumination conditions and location and settings of the camera were constant on a stand for all images (f/2.8, shutter speed 1/60 s). Calibration was based on a reference card (2 by 15 cm), described by Ozkaya and Bozkurt (20072008). A photographic survey was carried out on each animal from the lateral view. Images were saved on a flash card, and then transferred to a PC via a USB cable and processed by using Image Tools version 3.00 software to determine BM of animals. An animal electronic scale is used for weighing and imaging of the animal. A reference card was mounted and fixed on to the metal frame of the animal electronic scale. The measurements taken from the images were converted to cm. The linear parameters previously illustrated from a lateral photograph and the survey area of the lateral profile of BL, WH, CD, HH and BA were measured by utilising the DIA.

Statistical analyses Mean square prediction error (MSPE) was used for comparison of actual and predicted BM:

of 8 Publisher: CSIRO; Journal: AN:Animal Production Science Article Type: research-article; Volume: ; Issue: ; Article ID: AN14943 DOI: 10.1071/AN14943; TOC Head:

Where n is the number of pairs of actual and predicted values being compared, i = (1, 2, 3, ….,n),

Oi is the actual BM with ith variable and Pi is the predicted BM with ith variable.

The MSPE could be considered as the sum of three components described by Bibby and Toutenburg (1977):

Where and are the variance of actual and predicted BM, respectively. and are the means of the actual and predicted BM, b is the slope of the regression of actual values on predicted and r is the correlation coefficient between actual and predicted values.

The prediction equations for BW were obtained from BM; including BL, WH, CD, HH and BA. Descriptive statistics and regression analysis of BW on each of the independent variables were performed using regression analysis procedure of MINITAB. Correlation coefficients were also obtained among variables. Linear effects of independent variables on BW were included in the following model:

Where, Y is dependent variable, b0 is intercept, b1, b2, …. bn are the regression coefficients, X1, X2,

…. Xn are independent variables (BL, WH, CD, HH and BA), eij = residual error term.

Results In this study, WH, BL, CD and HH values of cattle were obtained both manually and by DIA. However, BA was obtained by DIA. Descriptive statistics were shown in Table 1.

Results showed that there were no statistically significant (P > 0.05) differences between the actual and predicted values for all BM and there was a substantial agreement between actual and predicted BM by using image analysis (Table 2).

Mean bias, calculated subtracting predicted from actual value, was positive (Table 2). Although image analysis overestimated BM, numerical differences between actual and predicted BM were statistically not significant (P > 0.05). Image analysis had a higher proportion of error derived from random then bias and line. A small proportion of line bias showed that the error derived from line bias was low and there was a minimal variation between predicted and actual BM.

The results showed that the DIA can be used confidently in order to determine the BM of Limousin cattle.

of 8 Publisher: CSIRO; Journal: AN:Animal Production Science Article Type: research-article; Volume: ; Issue: ; Article ID: AN14943 DOI: 10.1071/AN14943; TOC Head: The multiple and single regression equations obtained between BW and BM of DIA were shown in Table 3 where R2 were also given.

The regression analysis used for estimating BW including all BM gave the highest R2 value in Model 1 (0.89). HH and CD were statistically significant (P < 0.05). The regression equation in Model 1 explains 88.4% of BW variation. Model 1 and Model 3 were found to be very close to each other. The regression equations in Model 6 showed the lowest R2 value. However, R2 value increased in other models. The prediction equations in Model 10 obtained by DIA resulted in better prediction of BW than the equations including other BM. The lowest R2 was observed in Model 12 (49.8%). It was observed that Model 15 in the single regression equation highly increased R2 values (Table 3).

These results indicated that the regression equations in Models 11, 13, 14 and 15 showed that the prediction ability of DIA was better than that of the equation in Model 12. The relationship between BA and change in BW is presented in Table 3. The result showed that a 1-cm2 change in BA led to change in BW (–0.002 kg) in comparison to the other BM. Similarly, a 1 cm-increase in BL, WH, HH, and CD resulted in 7.67-, 13.1-, 13.9-, and 19.6-kg changes in weight (Table 3).

Discussions Results showed that there were no significant (P > 0.05) differences between the actual and predicted means for all BM. Mean bias was found to be positive and BM were overestimated. This result was in line with the findings of other authors (Bozkurt et al. 2007; Negretti et al. 2008; Ozkaya 2012) indicating that DIA was overestimating BM of animals.

The highest R2 values were obtained by using all BM as shown in Table 4 3(Model 1). The BW of Limousin cattle could be predicted accurately with the equation which in Model 1. According to Tasdemir et al. (2011), the maximum R2 value was obtained 95.8% in equation, which included WH, HH, BL and hip width (HW). However, the authors did not give the CD and BA values obtained by DIA. Ozkaya and Bozkurt (2008) indicated that the prediction capability of DIA was low when the animal had a large frame (such as the Holstein, R2 = 31.5%) as opposed to the data obtained in Brown Swiss and crossbred (R2 = 85.0% and 78.0%, respectively), which included BL, WH, HH, CD and BA.

Regression analysis between BA and CD resulted in an R2 value of 0.85. However, Bozkurt et al. (2007) indicated that the R2 value was found to be 55.0%, which obtained from equation contained same traitsBA and CD. Ozkaya and Bozkurt (2008) indicated that the R2 values were found to be 26.5%, 72.9% and 61.0% in Holstein, Brown Swiss and crossbred, respectively. Result showed that the regression equation in Model 10 resulted in better prediction of BW. However, Bozkurt et al. (2007) and Ozkaya and Bozkurt (2008) indicated that the regression equation including both BA and BL resulted in better prediction.

In this study, single regression equations can be used to estimate BW in Limousin cattle accurately. The highest R2 values were obtained in Model 15 (84.0%), Model 14 (73.4%), Model 13 (69.6%),

of 8 Publisher: CSIRO; Journal: AN:Animal Production Science Article Type: research-article; Volume: ; Issue: ; Article ID: AN14943 DOI: 10.1071/AN14943; TOC Head: Model 11 (61.5%) and Model 12 (49.8%). Bozkurt et al. (2007) indicated that the R2 values obtained were 63.6% for BL, 53.2% for WH and 52.1% for BA. Ozkaya and Bozkurt (2008) indicated that the highest R2 values were obtained from BL (28.6%, 80.7% and 75.6%), WH (25.5%, 61.6% and 63.7%), CD (22.1%, 72.9% and 50.0%), HH (19.7%, 51.3% and 63.3%) and BA (18.0%, 43.2% and 51.7%) in Holstein, Brown Swiss and crossbred cattle, respectively. Tasdemir et al. (2011) indicated that the R2 values obtained were 90.9% for HW, 88.8% for WH, 77.4% for BL and 73.5% for HH. Our results indicated that Model 15 can be used to predict BW accurately. However, Bozkurt et al. (2007) and Ozkaya and Bozkurt (2008) indicated that BL can be used to predict BW. On the contrary, Tasdemir et al. (2011) reported that HW and WH can be used to predict BW.

The regression equation in Model 11 can be used to predict BW. This result is in agreement with that reported by Bozkurt et al. (2007).

Conclusions In this study, it was shown that DIA appears to be a very reliable system for the estimation of BW of Limousin cattle. BM can be predicted by the DIA system with confidence and flexibility because there has been an acceptable agreement and close relationship between actual and predicted values. CD obtained with only one parameter by using DIA, can provide a considerably reliable prediction of BW. However, BA and CD values together are better parameters in predicting BW.

Acknowledgements We thank the Agricultural Research Centre of Technology and Life Science University and Suleyman Demirel University, Agriculture Faculty for supporting this study.

References Bibby J, Toutenburg H (1977) ‘Prediction and improved estimation in linear models.’ (John Wiley & Sons: London)

Bozkurt Y, Aktan S, Ozkaya S (2007) Body weight prediction using digital image analysis for slaughtering beef cattle. Journal of Applied Animal Research 32, 195–198. doi:10.1080/09712119.2007.9706877

Bozkurt Y, Aktan S, Ozkaya S (2008) Digital image analysis to predict carcass weight and some carcass characteristics of beef cattle. Asian Journal of Animal and Veterinary Advances 3(3), 129–137. doi:10.3923/ajava.2008.129.137

McDonald TP, Chen YR (1990) Separating connected muscle tissues in images of beef carcass ribeyes. Transactions of the ASAE. American Society of Agricultural Engineers 33(6), 1259–1265. doi:10.13031/2013.31576

Negretti P, Bianconi G, Bartocci S, Terramoccia S, Noe L (2008) Determination of live weight and body condition score in lactating Mediterranean buffalo by visual image analysis. Livestock Science 113, 1–7. doi:10.1016/j.livsci.2007.05.018

of 8 Publisher: CSIRO; Journal: AN:Animal Production Science Article Type: research-article; Volume: ; Issue: ; Article ID: AN14943 DOI: 10.1071/AN14943; TOC Head: Ozkaya S (2012) Accuracy of body measurements using digital image analysis in female Holstein calves. Animal Production Science 52(10), 917–920. doi:10.1071/AN12006

Ozkaya S, Bozkurt Y (2008) The relationship of parameters of body measures and body weight by using digital image analysis in pre-slaughter cattle. Archiv Tierzucht. Arhives Animal Breeding 51(2), 120–128.

Polgar P, Szücs E, Szabo F (1997) Effect of selection for improvement of dairy performance on growth and body structure in Holstein Friesian and Hungarian Fleckvieh young bulls. Archiv Tierzucht. Arhives Animal Breeding 40, 505–510.

Tasdemir S, Urkmez A, Inal S (2011) Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis. Computers and Electronics in Agriculture 76, 189–197. doi:10.1016/j.compag.2011.02.001

Ulutas Z, Saatci M, Ozluturk A (2001) Prediction of body weight from body measurements in East Anatolian Red Calves. Journal of the Faculty of Agriculture. Erzurum University 32, 61–65.

Manuscript received 14 November 2014, accepted 28 June 2015 Table 1. Descriptive statistics of bodyweight and body measurements for measurements taken on Limousin cattle (n = 56) BW, bodyweight; WH, wither height; BL, body length; CD, chest depth; HH, hip height; BA, body area; s.e., standard error

Obtained by digital image Actual analysis Variables Mean ± s.e. Mean ± s.e. BW (kg) 616.7 ± 21.3 WH (cm) 127.9 ± 1.3 128.9 ± 1.3 BL (cm) 164.3 ± 2.1 165.6 ± 1.8 CD (cm) 69.1 ± 0.9 70.5 ± 0.9 HH (cm) 132.9 ± 1.3 133.8 ± 1.3 2 BA (cm ) – 17 223 ± 1371

of 8 Publisher: CSIRO; Journal: AN:Animal Production Science Article Type: research-article; Volume: ; Issue: ; Article ID: AN14943 DOI: 10.1071/AN14943; TOC Head: Table 2. Prediction accuracy of the image analysis and comparison between actual and predicted and mean square prediction error (MSPE) and proportion of MSPE (n = 56) BW, bodyweight; WH, wither height; BL, body length; CD, chest depth; HH, hip height; BA, body area; s.e., standard error; s.d., standard deviation; Var., variance

Proportion of MSPE

BM Mean s.e. s.d. Var. b R2 r Mean bias MSPE Bias Line Random BL Actual 164.3 2.0 15.2 233 – – – – – – – – Predicted 165.6 1.8 13.5 183 1.07 0.91 0.95 1.29 24.42 0.07 0.04 0.89 WH Actual 127.9 1.3 9.66 93.3 – – – – – – – – Predicted 128.9 1.3 9.97 99.4 0.96 0.98 0.99 1.05 3.13 0.35 0.05 0.60 CD Actual 69.1 0.9 6.96 48.5 – – – – – – – – Predicted 70.5 0.9 6.82 46.5 0.99 0.94 0.97 1.01 4.80 0.36 0.00 0.64 HH Actual 132.9 1.3 9.67 93.6 – – – – – – – – Predicted 133.8 1.3 9.63 92.8 0.99 0.97 0.99 0.44 3.24 0.25 0.00 0.75 Table 3. Multiple and single regression equations of bodyweight and linear effects of body measurements by digital image analysis *Statistically significant (P < 0.05), b is coefficient of variables, × is independent variables (BA, BL, WH, HH and CD); BW: bodyweight, WH: wither height, BL: body length, CD: chest depth, HH: hip high, BA: body area, s.e.: standard error

Prediction equations Model Constant BA BL WH HH CD R2 (cm2) %

Y = a + b1X1 + b2X2 + b3X3 + b4X4 1 –1088 –0.001 –0.67 –1.10 8.47* 11.9* 88.7 + b5X5 Y = a + b2X2 + b3X3 + b4X4 + b5X5 2 –1134 – –0.57 –1.23 8.90* 11.5* 87.6 Y = a + b1X1 + b3X3 + b4X4 + b5X5 3 –1113 –0.001 – –1.48 8.44* 11.5* 88.4 Y = a + b1X1 + b2X2 + b4X4 + b5X5 4 –1084 –0.001 0.74 – 7.61* 11.7* 86.6 Y = a + b1X1 + b2X2 + b3X3 + b5X5 5 –945 –0.001 0.63 5.98* – 13.0* 85.4 Y = a + b1X1 + b2X2 + b3X3 + b4X4 6 –1233 –0.000 0.66 2.63 10.5* – 74.5 Y = a + b1X1 + b2X2 7 –619 –0.001 7.58* – – – 67.0

of 8 Publisher: CSIRO; Journal: AN:Animal Production Science Article Type: research-article; Volume: ; Issue: ; Article ID: AN14943 DOI: 10.1071/AN14943; TOC Head:

Y = a + b1X1 + b3X3 8 –1038 –0.001 – 13.0* – – 70.8 Y = a + b1X1 + b4X4 9 –1223 –0.001 – – 13.8* – 74.1 Y = a + b1X1 + b5X5 10 –729 –0.002 – – – 19.5* 84.5 Y = a + b1X1 11 –656 – – – – – 61.5 0.002* Y = a + b2X2 12 –654 – 7.67* – – – 49.8 Y = a + b3X3 13 –1067 – – 13.1* – – 69.6 Y = a + b4X4 14 –1242 – – – 13.9* – 73.4 Y = a + b5X5 15 –766 – – – – 19.6* 84.0