Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
The present study consisted in verifying the effectiveness of the image analysis method for body measurement in dromedary camel compared to manual measurements as a reference method. To do this, twenty-one linear body measurements were estimated on 59 adult Sahraoui dromedary camels (22 males and 37 females) with normal clinical condition by the standard method using a measuring stick or vernier caliper. Image analysis on pro le, front or behind photographs were processed using Axiovision Software. Overall; mean comparison, relative error, variance, Pearson's correlation coe cient and coe cient of variance showed that the image analysis method is accurate in relation to the manual measurement. Furthermore, image analysis results indicated relevant accuracy (bias correction factor, Cb ≈1) and precision (Pearson ρ ≈ 1) which were signi cantly correlated with the results of the reference method (Lin's concordance correlation coe cients rccc ≈ 1). According to Blant Altman upper and lower limits of agreement, the concordance was estimated between 93.22 and 98.3%. Passing-Bablok regression showed good relationship between results of the two methods displaying no signi cant systematic and proportional bias. The image analysis method for linear body measurements in dromedary camel yielded results that are in agreement with the manual measuring method. This method is a valid tool for studies on camel conformation traits.
The present study consisted in verifying the effectiveness of the image analysis method for body measurement in dromedary camel compared to manual measurements as a reference method. To do this, twenty-one linear body measurements were estimated on 59 adult Sahraoui dromedary camels (22 males and 37 females) with normal clinical condition by the standard method using a measuring stick or vernier caliper. Image analysis on pro le, front or behind photographs were processed using Axiovision Software. Overall; mean comparison, relative error, variance, Pearson's correlation coe cient and coe cient of variance showed that the image analysis method is accurate in relation to the manual measurement. Furthermore, image analysis results indicated relevant accuracy (bias correction factor, Cb ≈1) and precision (Pearson ρ ≈ 1) which were signi cantly correlated with the results of the reference method (Lin's concordance correlation coe cients rccc ≈ 1). According to Blant Altman upper and lower limits of agreement, the concordance was estimated between 93.22 and 98.3%. Passing-Bablok regression showed good relationship between results of the two methods displaying no signi cant systematic and proportional bias. The image analysis method for linear body measurements in dromedary camel yielded results that are in agreement with the manual measuring method. This method is a valid tool for studies on camel conformation traits.
Accurate and efficient access to Mongolian horse body size information is an important component in the modernization of the equine industry. Aiming at the shortcomings of manual measurement methods, such as low efficiency and high risk, this study converts the traditional horse body measure measurement problem into a measurement keypoint localization problem and proposes a top-down automatic Mongolian horse body measure measurement method by integrating the target detection algorithm and keypoint detection algorithm. Firstly, the SimAM parameter-free attention mechanism is added to the YOLOv8n backbone network to constitute the SimAM–YOLOv8n algorithm, which provides the base image for the subsequent accurate keypoint detection; secondly, the coordinate regression-based RTMPose keypoint detection algorithm is used for model training to realize the keypoint localization of the Mongolian horse. Lastly, the cosine annealing method was employed to dynamically adjust the learning rate throughout the entire training process, and subsequently conduct body measurements based on the information of each keypoint. The experimental results show that the average accuracy of the SimAM–YOLOv8n algorithm proposed in this study was 90.1%, and the average accuracy of the RTMPose algorithm was 91.4%. Compared with the manual measurements, the shoulder height, chest depth, body height, body length, croup height, angle of shoulder and angle of croup had mean relative errors (MRE) of 3.86%, 4.72%, 3.98%, 2.74%, 2.89%, 4.59% and 5.28%, respectively. The method proposed in this study can provide technical support to realize accurate and efficient Mongolian horse measurements.
Morphological scoring is a common evaluation method for domestic animals. The National Association of Maremmano Breeders (ANAM) has provided a dataset containing the records of 600 horses, four metric measurements (cm) and 24 traits with a continuous evaluation scale, each one with 15 classes. Moreover, a body condition score (BCS) with five classes is included. In this study, factor analysis was conducted to create a small number of informative factors (3) obtained from these traits, and a new BLUP-AM-MT index was established. The New Estimated Breeding Value (NEBV1) of each horse was computed by adding the genetic indexes of the three factors, with each one multiplied using a coefficient indicated by ANAM. The practical feasibility of the NEBV1 was evaluated through Spearman correlations between the rankings of the NEBV1 and the rankings of the BLUP-AM-MT, estimated through the four biometric measures and the morphological score (MS) assigned to each horse by the ANAM judges. The factorial analysis was used to estimate three factors: the “Trunk Dimension”, “Legs” and “Length”. As the explained variance was only 32%, the model was rotated, and the heritability of the three factors were 0.51, 0.05 and 0.41, respectively. After rotation, the estimated correlations between the new NEBV1 and the biometric measures were improved. These results should encourage breeders to adopt a breeding value index that takes into consideration the factors derived from all the variables observed in the morphological evaluation of the Maremmano. In this way, breeders can use it to select the best animals for breeding.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.