While new body measurement technologies are making great advances in certain species, such as cattle (Negretti et al., 2008; Bewley et al., 2008; Fischer et al., 2015; Kuzuhara et al. ., 2015; Le Cozler et al., 2019; Ruchay et al., 2020; Kojima et al 2022), they are still in full development in equines (Freitag et al., 2021), pig (Schofield et al., 1999; Zhang et al., 2021), sheep (Zhang et al., 2018a, b) and also bees (Khedim et al., 2021; Benghalem et al., 2021). However, this method is all at the beginning of experimentation in the camel species, which is still exploited mainly in traditional systems (Iglesias et al., 2020; Çağlı and Yılmaz 2021). Access to morphological evaluation using image analysis in this species is of particular interest because of difficulties in handling and restraining, its large size and mobile or even aggressive behavior (Iglesias et al., 2020; Alhajeri et al., 2021). Body measurements in these species require one to two people to immobilize the animal and two other people to take body measurements (Meghelli et al., 2020). Using the manual method is very time-consuming and is not without risk for the handlers and the animal. Indeed, this technology would be highly recommended to assess camels' morphological changes and body condition during the different stages of lactation and also to determine the conformation, the profile, and the format of camels intended for slaughter or the monitoring of young animals in growth. Finally, this technology is also interesting for determining the animals' live weight when prescribing drugs or calculating individual needs when formulating rations.
The present study investigates the effectiveness of the image analysis method by checking the hypothesis that this method offers a fine, precise, non-subjective analysis of linear body measurement in dromedary camels. To do this, we tried to determine the relative error of variance, concordance correlation, and possible sources of bias in the results of the two methods for each parameter. The morphological criteria take the animal into consideration in its length, width, height, and depth by examining the images taken from the profile, front, and behind positions.
Linear regression between all the measurements taken by the reference method and those obtained by image analysis revealed a significant high R2 (P <0.001) and a coefficient of variation (VC) of less than 10%. This indicates that the results of the two methods are quite correlated. However, high correlation does not necessarily mean agreement since the correlation coefficient cannot detect systematic bias (McAlinden et al, 2011). A non-significant difference in the mean values between the two body measurement methods was recorded for eleven parameters (P > 0.05). The VC is calculated as an indicator which quantifies the variability of a quantitative characteristic measured several times compared to the mean of this characteristic calculated from these same measurements. Recently; Çağlı and Yılmaz (2021) have compared the body measurements between manual measuring method and photographic and 3D methods in dromedary species. These authors showed that the 3D method is a more reliable, easy, and practical method for body measurements. The accuracy of image analysis compared to manual measurements was lower, indicated by a significant difference between the results of the two methods for height at withers, back height, rump height, body length, shoulder width, and rump width (Çağlı and Yılmaz 2021). According to these authors, only rump width was statistically non different between the two methods (Çağlı and Yılmaz 2021). These results are different from ours except for shoulder width. In addition, we have found that the results of the following parameters are statistically no different: chest depth, width at the trochanter, length of the posterior limb, neck length, hump length, and hump width. When using 3D technology for zoobiometry in dromedary camels, Çağlı and Yılmaz (2021) found that there was a significant difference between the 3D analysis method and manual measurements only for two parameters, which are brisket height and abdominal height. Differences between our results and those obtained by Çağlı and Yılmaz (2021) would be due to the image analysis process. On the other hand, the results obtained by Fischer et al. (2015) and Le Cozler et al. (2018) on cattle indicate that the measurements using a photographic support provide good precision (correlation between 0.78 and 0.89 for the chest depth, hip width, and circumference chest) and also good repeatability and reproducibility (CVr = 2.91% and CVR = 3.95%, respectively) except for ischial Width, where both Ingenera and Morpho 3D devices do not give reliable results for this parameter (Le Cozler et al. 2018). Nowadays, analyses of the repeatability and reproducibility of zoobiometry on photographic images in dromedaries are non-existent.
Furthermore, the mean comparison and variation coefficient level of manual measurements and image analysis should be taken with caution as they don’t provide proper conclusions. Indeed, the two averages can be equal to two completely discordant series. It compares the means of two samples, and the results will reveal a constant but not proportional difference between the two sets of measurements (Bilić-Zulle 2011). The VC <10% does not necessarily indicate that the N values measured on an individual are close to each other. Likewise, the correlation between the values of two different methods could be significantly high, but the two methods are not concordant. Correlation describes the linear relationship between two sets of data but not their agreement (Udovičić et al., 2007); it does not detect if there is a constant or proportional difference between the two methods. Linear regression presumes that comparative method results are measured without error (Linnet 1993). Therefore, it is more appropriate to analyze the correlation between these methods using Lin's concordance method on individuals measured only once by the same operator to determine agreement between methods (Lin 1989; Barnhart et al., 2007). Furthermore, the graphical method of Bland and Altman was used because it is based on the definition of the concordance between two series of measurements (Bland and Altman, 1986). The two series are concordant if one doesn’t overestimate or underestimate the other significantly. The Bland-Altman analysis enables the determination of systematic bias between manual measurement as a reference method and an image analysis method by calculating the mean difference between measurement results. It allows the calculation of limits of agreement, which allows the estimation of the total bias consisting of systematic and random bias (Bland and Altman 1986; Bland and Altman 1999). We found some studies that used such data analysis methods to describe agreement between methods to evaluate body measurements, claw conformation, udder traits, and body composition in dairy cattle automated devices (Alawneh et al., 2011; Song et al., 2014; Laven et al., 2015; Bell et al., 2018; Shorten 2021). However, we didn't find such analysis for body measurement using different methods in camel species. Furthermore, similar approaches were used to estimate the concordance to predict body fat percentage and body mass index categories in humans using different methods and devices (Affuso et al., 2018; Kogure et al., 2020; Lahav et al., 2021).
In our study, we observed a perfect level of Lin's coefficient of concordance for all morphological parameters except EL. This indicates that the differences between the abscissa (image analysis method) and ordinate (reference method) points and the 45 ° line (line of equality) are small, which is represented by Cb values (accuracy) close to 1 (Sup. Figures 2). It was also observed that there is usually a low bias between measurements and narrow limits of agreement concordance ranging between 93.22 and 98.3% (Sup. figures 2). Since measurement errors had to be assumed in both comparison and testing methods, the Passing-Bablok regression was preferred over ordinary linear regression (Passing and Bablok 1983). Passing and Bablok regression analysis allows valuable estimation of the analytical methods' agreement and possible systematic bias between them. It is robust and non sensitive to the distribution of errors and data outliers (Bilić-Zulle 2011). Reference and estimated methods produced accurate results for all studied morphological parameters. Based on the reported 95% limits of agreement, the slope reveals that there is no systematic difference [-7.31; 9.61] and also no proportional difference (from 0 to 21%) between the two means of measurement (Sup. figures 2). Only the EL study showed high systematic and proportional bisection values of around 7.66 cm and 67%, respectively (Sup. Figure 3). Based on similar data analysis, Zhang et al. (2018a) indicated that the method based on visual image analysis is effective, and it is especially suitable for sheep feeding in an intensive and large scale way.
The results have shown that the use of image analysis as a method of measuring linear body measurements using photos taken from profile, front, and behind positions allowed us to obtain results with low coefficients of variation and high correlation apart from the ear length. All statistical analyses confirmed a very good concordance between the two series of measurements, with low bias and no systematic or proportional difference between the two measurement methods.
Our study has clearly shown that, by the numerical method, nearly all the measurements can be estimated with precision. These results indicate that the image body measuring method is easier to implement than the manual method and also has the advantages of less workload and impersonal impartiality at high speed while working in safety, especially in difficult conditions. The next step of our study would be to improve the conditions for taking photos so we could get a better magnification of the technique.