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Medical and healthcare institutions have embraced the utilization of fetal monitoring systems to evaluate the condition of fetus throughout pregnancy, with a particular emphasis on the labor and delivery phases. Technological advancements have facilitated the early detection of defects and irregularities in the initial stages of pregnancy, thereby amplifying the possibility of successful interventions. The primary objective of this study is to develop a system capable of detecting abnormalities in the fetus by analyzing ultrasound images obtained during routine ultrasonography sessions within the first 21 weeks of pregnancy. The developed system evaluates crucial parameters such as the dimensions of the head, abdomen, and femur, as well as the fetal weight. The diagnostic procedure encompasses the segmentation of the fetus's head, abdomen, and femur regions, followed by the extraction of feature descriptors from these segmented areas. To categorize these features as normal or abnormal, a Support Vector Machine (SVM) classifier is employed. Furthermore, the methodology is capable of detecting any structural anomalies that may be present. Notably, this approach has yielded an impressive accuracy rate of 95.238%, thus offering healthcare practitioners a valuable diagnostic aid. [ABSTRACT FROM AUTHOR]

The weight of live pigs is directly related to their health, nutrition management, disease prevention and control, and the overall economic benefits to livestock enterprises. Direct weighing can induce stress responses in pigs, leading to decreased productivity. Therefore, modern livestock industries are increasingly turning to non-contact techniques for estimating pig weight, such as automated monitoring systems based on computer vision. These technologies provide continuous, real-time weight-monitoring data without disrupting the pigs' normal activities or causing stress, thereby enhancing breeding efficiency and management levels. Two methods of pig weight estimation based on image and point cloud data are comprehensively analyzed in this paper. We first analyze the advantages and disadvantages of the two methods and then discuss the main problems and challenges in the field of pig weight estimation technology. Finally, we predict the key research areas and development directions in the future. [ABSTRACT FROM AUTHOR]

In forensic and archaeological contexts, accurately determining an individual's age, sex, height, and weight is essential for the identification of unidentified bodies and dismembered remains. This study assesses the effectiveness of linear regression analysis and artificial neural networks (ANNs) in estimating body weight using foot measurements within the Eastern Turkish population. The research was conducted with medical students, including 149 volunteers--76 males and 73 females. Participants' height and weight were recorded using a stadiometer. Foot measurements were taken with a Vernier caliper and an osteometric board. The data were assessed utilizing SPSS 26.0, and ANN models were developed using the Keras library within the Google Colab platform. In terms of height, weight, and foot size, all these values were significantly higher in males than in females. Linear regression methods resulted in a root mean squared error (RMSE) of 11.83 kg for males, 6.23 kg for females, and 9.56 kg for the entire sample. With ANNs, these error values were reduced to 10.13 kg for males, 5.10 kg for females, and 8.07 kg for the entire sample. The analysis shows that ANNs are more adept at handling complex data, leading to improved prediction accuracy. [ABSTRACT FROM AUTHOR]

In the livestock industry, reducing the cost of raising animals while maintaining quality is of utmost importance. Growth management is crucial for livestock quality control, with weight being a primary focus. This study proposes a method for estimating the weight of a breed sow from point clouds using a convolutional neural network (CNN). The proposed method consists of two stages: first, 2D images are generated from point clouds of a walking breed sow; next, these images are used as input in a CNN model to estimate the weight. The point clouds are divided into two halves along a longitudinal plane. Then, distances are calculated between each element of the 128 × 64 rectangular mesh created on the aforementioned plane and the corresponding points in the divided point cloud. A 2D image is generated by varying the grayscale intensity according to the calculated distances. Three types of CNNs, namely, VGG16, DenseNet121, and EfficientNet B0, were employed to estimate the weight using the generated 2D images as input. The CNNs were trained using approximately 150,000 images of 71 pigs. To verify the effectiveness of the method, the trained networks were used to conduct weight estimation experiments on test cases. The results of the experiments demonstrate a high weight estimation accuracy, with an error rate as low as 1.35%.

Abstract Background Weight estimation is required in adult patients when weight‐based medication must be administered during emergency care, as measuring weight is often not possible. Inaccurate estimations may lead to inaccurate drug dosing, which may cause patient harm. High‐tech 3D camera systems driven by artificial intelligence might be the solution to this problem. The aim of this review was to describe and evaluate the published literature on 3D camera weight estimation methods. Methods A systematic literature search was performed for articles that studied the use of 3D camera systems for weight estimation in adults. Data on the study characteristics, the quality of the studies, the 3D camera methods evaluated, and the accuracy of the systems were extracted and evaluated. Results A total of 14 studies were included, published from 2012 to 2024. Most studies used Microsoft Kinect cameras, with various analytical approaches to weight estimation. The 3D camera systems often achieved a P10 of 90% (90% of estimates within 10% of actual weight), with all systems exceeding a P10 of 78%. The studies highlighted a significant potential for 3D camera systems to be suitable for use in emergency care. Conclusion The 3D camera systems offer a promising method for weight estimation in emergency settings, potentially improving drug dosing accuracy and patient safety. Weight estimates were satisfactorily accurate, often exceeding the reported accuracy of existing weight estimation methods. Importantly, 3D camera systems possess characteristics that could make them very appropriate for use during emergency care. Future research should focus on developing and validating this methodology in larger studies with true external and clinical validation.

Accurate feeding management in aquaculture relies on assessing the average weight of aquatic animals during their growth stages. The traditional method involves using a labor-intensive approach and may impact the well-being of fish. The current research focuses on a unique way of estimating red tilapia’s weight in cage culture via a river, which employs unmanned aerial vehicle (UAV) and deep learning techniques. The described approach includes taking pictures by means of a UAV and then applying deep learning and machine learning algorithms to them, such as convolutional neural networks (CNNs), extreme gradient boosting (XGBoost), and a Hybrid CNN-XGBoost model. The results showed that the CNN model achieved its accuracy peak after 60 epochs, showing accuracy, precision, recall, and F1 score values of 0.748 ± 0.019, 0.750 ± 0.019, 0.740 ± 0.014, and 0.740 ± 0.019, respectively. The XGBoost reached its accuracy peak with 45 n_estimators, recording values of approximately 0.560 ± 0.000 for accuracy and 0.550 ± 0.000 for precision, recall, and F1. Regarding the Hybrid CNN-XGBoost model, it demonstrated its prediction accuracy using both 45 epochs and n_estimators. The accuracy value was around 0.760 ± 0.019, precision was 0.762 ± 0.019, recall was 0.754 ± 0.019, and F1 was 0.752 ± 0.019. The Hybrid CNN-XGBoost model demonstrated the highest accuracy compared to using standalone CNN and XGBoost models and could reduce the time required for weight estimation by around 11.81% compared to using the standalone CNN. Although the testing results may be lower than those from previous laboratory studies, this discrepancy is attributed to the real-world testing conditions in aquaculture settings, which involve uncontrollable factors. To enhance accuracy, we recommend increasing the sample size of images and extending the data collection period to cover one year. This approach allows for a comprehensive understanding of the seasonal effects on evaluation outcomes.

In forensic and archaeological contexts, accurately determining an individual's age, sex, height, and weight is essential for the identification of unidentified bodies and dismembered remains. This study assesses the effectiveness of linear regression analysis and artificial neural networks (ANNs) in estimating body weight using foot measurements within the Eastern Turkish population. The research was conducted with medical students, including 149 volunteers—76 males and 73 females. Participants' height and weight were recorded using a stadiometer. Foot measurements were taken with a Vernier caliper and an osteometric board. The data were assessed utilizing SPSS 26.0, and ANN models were developed using the Keras library within the Google Colab platform. In terms of height, weight, and foot size, all these values were significantly higher in males than in females. Linear regression methods resulted in a root mean squared error (RMSE) of 11.83 kg for males, 6.23 kg for females, and 9.56 kg for the entire sample. With ANNs, these error values were reduced to 10.13 kg for males, 5.10 kg for females, and 8.07 kg for the entire sample. The analysis shows that ANNs are more adept at handling complex data, leading to improved prediction accuracy. [Med-Science 2024; 13(3.000): 583-9]

Accurate feeding management in aquaculture relies on assessing the average weight of aquatic animals during their growth stages. The traditional method involves using a labor-intensive approach and may impact the well-being of fish. The current research focuses on a unique way of estimating red tilapia's weight in cage culture via a river, which employs unmanned aerial vehicle (UAV) and deep learning techniques. The described approach includes taking pictures by means of a UAV and then applying deep learning and machine learning algorithms to them, such as convolutional neural networks (CNNs), extreme gradient boosting (XGBoost), and a Hybrid CNN-XGBoost model. The results showed that the CNN model achieved its accuracy peak after 60 epochs, showing accuracy, precision, recall, and F1 score values of 0.748 ± 0.019, 0.750 ± 0.019, 0.740 ± 0.014, and 0.740 ± 0.019, respectively. The XGBoost reached its accuracy peak with 45 n_estimators, recording values of approximately 0.560 ± 0.000 for accuracy and 0.550 ± 0.000 for precision, recall, and F1. Regarding the Hybrid CNN-XGBoost model, it demonstrated its prediction accuracy using both 45 epochs and n_estimators. The accuracy value was around 0.760 ± 0.019, precision was 0.762 ± 0.019, recall was 0.754 ± 0.019, and F1 was 0.752 ± 0.019. The Hybrid CNN-XGBoost model demonstrated the highest accuracy compared to using standalone CNN and XGBoost models and could reduce the time required for weight estimation by around 11.81% compared to using the standalone CNN. Although the testing results may be lower than those from previous laboratory studies, this discrepancy is attributed to the real-world testing conditions in aquaculture settings, which involve uncontrollable factors. To enhance accuracy, we recommend increasing the sample size of images and extending the data collection period to cover one year. This approach allows for a comprehensive understanding of the seasonal effects on evaluation outcomes. [ABSTRACT FROM AUTHOR]

Precise detection and localization are prerequisites for intelligent harvesting, while fruit size and weight estimation are key to intelligent orchard management. In commercial banana orchards, it is necessary to manage the growth and weight of banana bunches so that they can be harvested in time and prepared for transportation according to their different maturity levels. In this study, in order to reduce management costs and labor dependence, and obtain non-destructive weight estimation, we propose a method for localizing and estimating banana bunches using RGB-D images. First, the color image is detected through the YOLO-Banana neural network to obtain two-dimensional information about the banana bunches and stalks. Then, the three-dimensional coordinates of the central point of the banana stalk are calculated according to the depth information, and the banana bunch size is obtained based on the depth information of the central point. Finally, the effective pixel ratio of the banana bunch is presented, and the banana bunch weight estimation model is statistically analyzed. Thus, the weight estimation of the banana bunch is obtained through the bunch size and the effective pixel ratio. The R2 value between the estimated weight and the actual measured value is 0.8947, the RMSE is 1.4102 kg, and the average localization error of the central point of the banana stalk is 22.875 mm. The results show that the proposed method can provide bunch size and weight estimation for the intelligent management of banana orchards, along with localization information for banana-harvesting robots. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent system for the dynamic estimation of sheep body weight (BW). The methodology used to estimate body weight is based on measuring seven biometric parameters: height at withers, rump height, body length, body diagonal length, total body length, semicircumference of the abdomen, and semicircumference of the girth. A biometric parameter acquisition system was developed using a Kinect as a sensor. The results were contrasted with measurements obtained manually with a flexometer. The comparison gives an average root mean square error (RMSE) of 9.91 and a mean R 2 of 0.81. Subsequently, the parameters were used as input in a back-propagation artificial neural network. Performance tests were performed with different combinations to make the best choice of architecture. In this way, an intelligent body weight estimation system was obtained from biometric parameters, with a 5.8% RMSE in the weight estimations for the best architecture. This approach represents an innovative, feasible, and economical alternative to contribute to decision-making in livestock production systems. [ABSTRACT FROM AUTHOR]

Simple Summary: The volume of a pig, a new phenotype feature, can be used to estimate its weight due to its high correlation with body weight. The proportions of different body parts, such as the head and legs, can be determined through point cloud segmentation, providing new phenotype information for breeding pigs with smaller heads and stouter legs. However, due to the irregular shape and potential missing parts of the pig point cloud, it is challenging to form a closed surface for volume calculation. The study addresses this challenge by using an improved Poisson reconstruction algorithm, which provides smoother, more continuous, and complete reconstruction results, and its accuracy and reliability have been confirmed. The study also found that the correlation coefficient between pig body volume and weight was 0.95, indicating a strong relationship. This research could be valuable for improving the efficiency and accuracy of livestock weight estimation and breeding. Pig point cloud data can be used to digitally reconstruct surface features, calculate pig body volume and estimate pig body weight. Volume, as a pig novel phenotype feature, has the following functions: (a) It can be used to estimate livestock weight based on its high correlation with body weight. (b) The volume proportion of various body parts (such as head, legs, etc.) can be obtained through point cloud segmentation, and the new phenotype information can be utilized for breeding pigs with smaller head volumes and stouter legs. However, as the pig point cloud has an irregular shape and may be partially missing, it is difficult to form a closed loop surface for volume calculation. Considering the better water tightness of Poisson reconstruction, this article adopts an improved Poisson reconstruction algorithm to reconstruct pig body point clouds, making the reconstruction results smoother, more continuous, and more complete. In the present study, standard shape point clouds, a known-volume Stanford rabbit standard model, a measured volume piglet model, and 479 sets of pig point cloud data with known body weight were adopted to confirm the accuracy and reliability of the improved Poisson reconstruction and volume calculation algorithm. Among them, the relative error was 4% in the piglet model volume result. The average absolute error was 2.664 kg in the weight estimation obtained from pig volume by collecting pig point clouds, and the average relative error was 2.478%. Concurrently, it was determined that the correlation coefficient between pig body volume and pig body weight was 0.95. [ABSTRACT FROM AUTHOR]

Simple Summary: Pig farming plays a critical role in global animal husbandry, with pig weight and body dimensions serving as key indicators of growth and development. Manual measurement methods currently used face challenges like herding difficulties and stress in pigs. To address this, this study introduces a non-contact weight estimation and body measurement model using point cloud data from pig backs. By utilizing a depth camera and advanced algorithms, accurate weight predictions and body size measurements were achieved. The model showed promising results, with reduced errors compared to manual methods, demonstrating its potential for improving efficiency and animal welfare in pig farming practices. Pig farming is a crucial sector in global animal husbandry. The weight and body dimension data of pigs reflect their growth and development status, serving as vital metrics for assessing their progress. Presently, pig weight and body dimensions are predominantly measured manually, which poses challenges such as difficulties in herding, stress responses in pigs, and the control of zoonotic diseases. To address these issues, this study proposes a non-contact weight estimation and body measurement model based on point cloud data from pig backs. A depth camera was installed above a weighbridge to acquire 3D point cloud data from 258 Yorkshire–Landrace crossbred sows. We selected 200 Yorkshire–Landrace sows as the research subjects and applied point cloud filtering and denoising techniques to their three-dimensional point cloud data. Subsequently, a K-means clustering segmentation algorithm was employed to extract the point cloud corresponding to the pigs' backs. A convolutional neural network with a multi-head attention was established for pig weight prediction and added RGB information as an additional feature. During the data processing process, we also measured the back body size information of the pigs. During the model evaluation, 58 Yorkshire–Landrace sows were specifically selected for experimental assessment. Compared to manual measurements, the weight estimation exhibited an average absolute error of 11.552 kg, average relative error of 4.812%, and root mean square error of 11.181 kg. Specifically, for the MACNN, incorporating RGB information as an additional feature resulted in a decrease of 2.469 kg in the RMSE, a decrease of 0.8% in the MAPE, and a decrease of 1.032 kg in the MAE. Measurements of shoulder width, abdominal width, and hip width yielded corresponding average relative errors of 3.144%, 3.798%, and 3.820%. In conclusion, a convolutional neural network with a multi-head attention was established for pig weight prediction, and incorporating RGB information as an additional feature method demonstrated accuracy and reliability for weight estimation and body dimension measurement. [ABSTRACT FROM AUTHOR]

Objectives: The aim of the present study was to assess the accuracy of weight estimation in cats provided by pet owners, veterinary technicians, house officers (interns and residents) and attending clinicians in an emergency room (ER). An additional objective was to determine whether carrying the cat contributed to a more accurate weight estimate. Methods: A total of 72 cats presented to an ER and were enrolled in the study. Pet owners, veterinary technicians, house officers and attending clinicians were asked to record the cats' estimated weights on individual data collection cards. The actual weights of the cats were then obtained and compared with participants' estimations. Results: There were no significant differences between weight estimates provided by pet owners, veterinary technicians, house officers and attending clinicians. Similarly, neither the length of experience of the veterinary staff nor carrying the cat had an effect on the provision of a more accurate weight estimate. Conclusions and relevance: Pet owners are no better at weight estimation of cats in the ER setting compared with veterinary professionals. Carrying the cat did not improve the accuracy of weight estimation among veterinary professionals. The mean cat weight of 4.9 kg could be used as an average cat weight in an emergency situation for an adult cat. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate weight estimation tools using mid upper arm circumference (MUAC) and body length, and determine accuracy and precision of Broselow tape in children 6 mo to 15 y of age. Methods: Data of 18,456 children aged 6 mo to 5 y and 1420 children aged 5 to 15 y were used to develop linear regression equations using length and MUAC to estimate weight. These were validated on prospectively enrolled populations of 276 and 312 children, respectively. Accuracy was measured by Bland-Altman bias, median percentage errors, and percentage of predicted weight within 10% of true weight. Broselow tape was tested on the validation population. Results: Gender specific equations were developed which estimated weight within 10% of true weight in 69.9% (64.1–75.2%) and 65.7% (60.1–70.9%) of children aged 6 mo to 5 y, and 5 to 15 y, respectively. Broselow tape predicted weight within 10% of the true weight in 40.5% (34.7–46.6%) and 32.5% (26.7–38.7%) of children aged 6 mo to 5 y and 5 to 15 y, respectively. Conclusions: The model developed from MUAC and length accurately estimated weight in children aged 6 mo to 15 y, and is potentially useful during emergencies. The Broselow tape frequently overestimated weight in authors' setting. [ABSTRACT FROM AUTHOR]

Abstract Background Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children. The Michaelis–Menten equation, used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. We investigated whether this equation could be used to interpolate missing growth data in children in the first three years of life and compared this interpolation to several common interpolation methods and pediatric growth models. Methods We developed a modified Michaelis–Menten equation and compared expected to actual growth, first in a local birth cohort (N = 97) then in a large, outpatient, pediatric sample (N = 14,695). Results The modified Michaelis–Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22 kg [IQR:0.19; 90%

The weight of live pigs is directly related to their health, nutrition management, disease prevention and control, and the overall economic benefits to livestock enterprises. Direct weighing can induce stress responses in pigs, leading to decreased productivity. Therefore, modern livestock industries are increasingly turning to non-contact techniques for estimating pig weight, such as automated monitoring systems based on computer vision. These technologies provide continuous, real-time weight-monitoring data without disrupting the pigs’ normal activities or causing stress, thereby enhancing breeding efficiency and management levels. Two methods of pig weight estimation based on image and point cloud data are comprehensively analyzed in this paper. We first analyze the advantages and disadvantages of the two methods and then discuss the main problems and challenges in the field of pig weight estimation technology. Finally, we predict the key research areas and development directions in the future.

Weight estimation is required in adult patients when weight-based medication must be administered during emergency care, as measuring weight is often impossible. Inaccurate estimations may lead to inaccurate drug doses, which may cause patient harm. Little is known about the relative accuracy of different methods of weight estimation that could be used during resuscitative care. The aim of this study was to evaluate the performance and suitability of existing weight estimation methods for use in adult emergency care. A systematic literature search was performed for suitable articles that studied the accuracy of weight estimation systems in adults. The study characteristics, the quality of the studies, the weight estimation methods evaluated, the accuracy data, and any information on the ease-of-use of the method were extracted and evaluated. A total of 95 studies were included, in which 27 different methods of total body weight estimation were described, with 42 studies included in the meta-analysis. The most accurate methods, determined from the pooled estimates of accuracy (the percentage of estimates within 10% of true weight, with 95% confidence intervals) were 3-D camera estimates (88.8% (85.8 to 91.8%)), patient self-estimates (88.7% (87.7 to 89.7%)), the Lorenz method (77.5% (76.4 to 78.6%)) and family estimates (75.0% (71.5 to 78.6%)). However, no method was without significant potential limitations to use during emergency care. Patient self-estimations of weight were generally very accurate and should be the method of choice during emergency care, when possible. However, since alternative estimation methods must be available when confused, or otherwise incapacitated, patients are unable to provide an estimate, alternative strategies of weight estimation should also be available. [ABSTRACT FROM AUTHOR]

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The quality of radish roots depends largely on its cultivar, production environment, and postharvest management along the supply chain. Quality monitoring of fresh products is of utmost importance during the postharvest period. The purpose of this study is to nondestructively estimate the weight of a radish using random forests based on color and shape information obtained from images, as well as volumetric information obtained by analyzing a point cloud obtained by combining multiple forms of shape information. The explanatory variables were color and shape information obtained through an image analysis of still images of radishes captured in a constructed photographic environment. The volume information was calculated from the bounding box and convex hull applied to the point cloud by combining the shape information obtained from the image analysis. We then applied random forests to relate the radish weight to the explanatory variables. The experimental results showed that the models using color, shape, or volume information all exhibited good performance with a Pearson's correlation coefficient (COR) ≥ 0.80, suggesting the potential of nondestructive monitoring of radish weight based on color, shape, and volume information. Specifically, the model using volume information showed very high performance, with a COR of 0.95 or higher. [ABSTRACT FROM AUTHOR]

Simple Summary: Monitoring the weight of farm pigs is crucial for their well-being. Implementing Cameras and Machine Vision Systems shows promise for automating this process. Traditionally, studies have focused on RGB and depth images for weight prediction, using measurements like volume and body area. However, these methods prove less robust in fluctuating environmental conditions, especially lighting. This study reveals that PointNet, a 3D deep learning architecture trained on point cloud data (3D points), outperforms the conventional approach, and demonstrates stability in varying light conditions due to its ability to learn on spatial information. This finding underscores the potential for PointNet to significantly improve the accuracy and reliability of weight monitoring in farm settings. The selection of animals to be marketed is largely completed by their visual assessment, solely relying on the skill level of the animal caretaker. Real-time monitoring of the weight of farm animals would provide important information for not only marketing, but also for the assessment of health and well-being issues. The objective of this study was to develop and evaluate a method based on 3D Convolutional Neural Network to predict weight from point clouds. Intel Real Sense D435 stereo depth camera placed at 2.7 m height was used to capture the 3D videos of a single finishing pig freely walking in a holding pen ranging in weight between 20–120 kg. The animal weight and 3D videos were collected from 249 Landrace × Large White pigs in farm facilities of the FZEA-USP (Faculty of Animal Science and Food Engineering, University of Sao Paulo) between 5 August and 9 November 2021. Point clouds were manually extracted from the recorded 3D video and applied for modeling. A total of 1186 point clouds were used for model training and validating using PointNet framework in Python with a 9:1 split and 112 randomly selected point clouds were reserved for testing. The volume between the body surface points and a constant plane resembling the ground was calculated and correlated with weight to make a comparison with results from the PointNet method. The coefficient of determination (R2 = 0.94) was achieved with PointNet regression model on test point clouds compared to the coefficient of determination (R2 = 0.76) achieved from the volume of the same animal. The validation RMSE of the model was 6.79 kg with a test RMSE of 6.88 kg. Further, to analyze model performance based on weight range the pigs were divided into three different weight ranges: below 55 kg, between 55 and 90 kg, and above 90 kg. For different weight groups, pigs weighing below 55 kg were best predicted with the model. The results clearly showed that 3D deep learning on point sets has a good potential for accurate weight prediction even with a limited training dataset. Therefore, this study confirms the usability of 3D deep learning on point sets for farm animals' weight prediction, while a larger data set needs to be used to ensure the most accurate predictions. [ABSTRACT FROM AUTHOR]

ABSTRACT: In the broiler-breeding industry, phenotype determination is critical. Leg weight is a fundamental indicator for breeding, and noninvasive testing technology can reduce damage to animals. This study proposes a broiler leg weight estimation system comprising a weight-estimation model and computed tomography (CT) acquisition equipment. The weight-estimation model can automatically process the scan results of live broiler chickens from the CT acquisition equipment. The weight-estimation model comprises an improved you-only-look-once (YOLOv5) segmentation algorithm and a random forest fitting network. The segmentation head was introduced into the YOLOv5 network, combined with a multiscale attention mechanism and an atrous spatial pyramid pooling architecture, and a new network model, YOLO- measuring chicken leg weight (YOLO-MCLW), was proposed to improve segmentation efficiency and accuracy. Morphological parameters were extracted from the obtained mask image, and a random forest network was used for fitting. The experiments show that the system exhibited an average absolute error of 7.27 g and an average percentage error of 4.82% in tests on 50 individual legs of 25 broiler chickens. The prediction R2 of broiler chicken legs can reaches 88.98%, the segmentation intersection over union result reaches 95.45%, and 37.04 images are processed per second. This system provides technical support for the part determination of broiler chickens in commercial breeding.

Objective: In order to solve the problem of manual weighting of dragon fruit, including time-consuming, laborious and expensive, an automated weight estimation method based on machine vision and machine learning was proposed in this research. Methods: Firstly, 106 dragon fruits were weighed, recorded and photographed, and images of dragon fruits were constructed. Secondly, binary images were obtained after denoising and segmentation. Moreover, the three features of pixel area, major axis pixel length and minor axis pixel length of dragon fruits were extracted on the basis of binary images. The three features of each image and their corresponding weights were combined into a set of data, which was divided into training set and test set according to the ratio of 7∶3. Finally, the training set was input into the Gradient Boosting, Random Forest, K-Neighbors and Artificial Neural Networks machine-learning models for training, and the test sets were used for model evaluation. Results: The evaluation index of the Artificial Neural network performed well compared with other models, with R2 of 0.986 and RMSE of 13.091. Conclusion: The experimental result demonstrates that the method proposed in this research can accomplish the weight estimation of dragon fruit effectively, and meet the weight estimation requirements of dragon fruit.

Objective: Taking Aksu apples as an example, a joint image optimal feature extraction and improved RBF neural network learning apple weight estimation method is designed to overcome the high cost and large error of manual grading and weighing. Methods: Firstly, an apple image acquisition system was established to obtain apple foreground image information. Secondly, the optimal subset extraction strategy for apple image feature sets was designed, by transforming the process of extracting the optimal subset into an objective function optimization problem, and an improved discrete locust optimization algorithm was designed to obtain the optimal apple image feature subset. Finally, a weight estimation model for apples based on RBF neural network learning was constructed, with the optimal feature subset as network input. The locust optimization algorithm was used to optimize the configuration of RBF neural network hyperparameters, to achieve effective estimation of apple weight. Results: The proposed apple weight estimation method had higher accuracy, with an average relative error rate of 1.23% for weight estimation. Conclusion: This method can effectively achieve apple weight estimation and can also be applied to other fruits with similar axisymmetric shapes for weight estimation.

Simple Summary: In the broiler farming industry, accurately weighing broilers is essential. Camera-based weighing systems offer a solution without requiring expensive platform weighers. However, existing computer vision methods for estimating broiler weight are limited to young broilers and become less accurate as broilers age due to increased density and weight imbalance. To tackle this, a new approach is introduced in this paper. It uses a segmentation network (Mask R-CNN) with depth images captured by a 3D camera to isolate broilers. Artificial and learned features are combined using a feature fusion module, and these features are used to estimate broiler weight through gradient boosting decision trees (GBDT). The method excels in accurately estimating the weight of individual broilers within complex scenes and holds promise for enhancing broiler weight estimation methods. Broiler weighing is essential in the broiler farming industry. Camera-based systems can economically weigh various broiler types without expensive platforms. However, existing computer vision methods for weight estimation are less mature, as they focus on young broilers. In effect, the estimation error increases with the age of the broiler. To tackle this, this paper presents a novel framework. First, it employs Mask R-CNN for instance segmentation of depth images captured by 3D cameras. Next, once the images of either a single broiler or multiple broilers are segmented, the extended artificial features and the learned features extracted by Customized Resnet50 (C-Resnet50) are fused by a feature fusion module. Finally, the fused features are adopted to estimate the body weight of each broiler employing gradient boosting decision tree (GBDT). By integrating diverse features with GBTD, the proposed framework can effectively obtain the broiler instance among many depth images of multiple broilers in the visual field despite the complex background. Experimental results show that this framework significantly boosts accuracy and robustness. With an MAE of 0.093 kg and an R2 of 0.707 in a test set of 240 63-day-old bantam chicken images, it outperforms other methods. [ABSTRACT FROM AUTHOR]

Accurate weight measurement is critical for monitoring the growth and well-being of cattle. However, the traditional weighing process, which involves physically placing cattle on scales, is labor-intensive and stressful for the animals. Therefore, the development of automated cattle weight prediction techniques assumes critical significance. This study proposes a weight prediction approach for Korean cattle using 3D segmentation-based feature extraction and regression machine learning techniques from incomplete 3D shapes acquired from real farm environments. Firstly, we generated mesh data of 3D Korean cattle shapes using a multiple-camera system. Subsequently, deep learning-based 3D segmentation with the PointNet network model was employed to segment 3D mesh data into two dominant parts: torso and center body. From these segmented parts, the body length, chest girth, and chest width of Korean cattle were extracted. Finally, we implemented five regression machine learning models (CatBoost regression, LightGBM, polynomial regression, random forest regression, and XGBoost regression) for weight prediction. To validate our approach, we captured 270 Korean cattle in various poses, totaling 1190 poses of 270 cattle. The best result was achieved with mean absolute error (MAE) of 25.2 kg and mean absolute percent error (MAPE) of 5.85% using the random forest regression model. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To determine the accuracy of pet owners, veterinary technicians, house officers (interns and residents), and attending clinicians at estimating dog weights in a veterinary emergency room. ANIMALS: 272 dogs weighing over 2 kg presenting to the emergency room between June 1 and July 29, 2022. METHODS: Pet owners, veterinary technicians, house officers, and attending clinicians recorded the dogs' weight estimations on individual data collection cards. Pet owners were also asked to estimate their dogs' weight during the triage period. The dogs' actual weights were then obtained and recorded. RESULTS: Pet owners were more accurate than veterinary professionals at providing weight estimates for dogs. Weight estimates were accurate to within 10% of the dogs' actual weights for 67.9% (181/267) of pet owners. Forty-one percent (112/270) of attending clinicians, 35.3% (95/269) of house officers, and 35.4% (96/271) of veterinary technicians' weight estimates were within 10% of the dog's actual weight. There was no difference noted in the length of veterinary experience and ability to closely estimate the patient's weight. Overall, veterinary professionals were more likely to closely estimate the weight of large dogs compared to small dogs. CLINICAL RELEVANCE: The pet owner is most likely to provide an accurate weight for dogs and questions about the dog's weight should be directed to the client for situations in which a weight cannot be rapidly obtained. [ABSTRACT FROM AUTHOR]

Multi-modal medical image fusion (MMIF) is used to merge multiple modes of medical images for better imaging quality and more comprehensive information, such that enhancing the reliability of clinical diagnosis. Since different types of medical images have different imaging mechanisms and focus on different pathological tissues, how to accurately fuse the information from various medical images has become an obstacle in image fusion research. In this paper, we propose a self-supervised subspace attentional framework for multi-modal image fusion, which is constructed by two sub-networks, i.e., the feature extract network and the feature fusion network. We implement a self-supervised strategy that facilitates the framework adaptively extracts the features of source images with the reconstruction of the fused image. Specifically, we adopt a subspace attentional Siamese Weighted Auto-Encoder as a feature extractor to extract the source image features including local and global features at first. Then, the extracted features are given into a weighted fusion decoding network to reconstruct the fused result, and the shallow features from the extractor are used to assist reconstruct the fused image. Finally, the feature extractor adaptively extracts the optimal features according to the fused results by simultaneously training the two sub-networks. Furthermore, to achieve better fusion results, we design a novel weight estimation in the weighted fidelity loss that measures the importance of each pixel by calculating a mixture of salient features and local contrast features of the image. Experiments demonstrate that our method gives the best results compared with other state-of-the-art fusion approaches. [ABSTRACT FROM AUTHOR]

Introduction: The performance of various weight estimation methods in children with sickle cell disease (SCD) and heart disease (HD) has not been studied. We aimed to determine and compare the accuracies of the Broselow, Mercy, PAWPER XL and PAWPER XL-MAC tapes in Ghanaian children with no known chronic diseases (controls), SCD and HD. Methods: We prospectively recruited 631 children (199 with HD, 209 SCD and 223 controls) from the Komfo Anokye Teaching Hospital (KATH). Their weights were estimated using the Broselow, Mercy, PAWPER XL and PAWPER XL-MAC tapes. These estimated weights were compared to measured weight using mean percentage error (MPE), the proportion of weight estimates within ±10% (P10) and ±20% (P20) of measured weight. Bland-Altman limits of agreement (LOA) were determined to assess the precision of weight estimation and agreement with measured weight. Results: The PAWPER XL, Mercy and PAWPER XL-MAC were the most accurate in all groups of children studied. All methods except the Broselow tape (BT), which performed best in the control group, had their best performance among children with SCD with negligible critical error rates (proportion of children with weight estimates > 20% of their actual weight). The P20 in the various groups of children using the BT were 88.36%, 80.21% and 51.10% respectively in the control, SCD and HD groups. The Mercy, PAWPER XL and PAWPER XL MAC tapes were generally above 90% in all groups. Discussion: The Mercy, PAWPER XL and PAWPER XL-MAC tapes performed significantly better than the BT in all groups of children studied. These methods of weight estimation performed best in children with SCD with very little critical error.

Precise detection and localization are prerequisites for intelligent harvesting, while fruit size and weight estimation are key to intelligent orchard management. In commercial banana orchards, it is necessary to manage the growth and weight of banana bunches so that they can be harvested in time and prepared for transportation according to their different maturity levels. In this study, in order to reduce management costs and labor dependence, and obtain non-destructive weight estimation, we propose a method for localizing and estimating banana bunches using RGB-D images. First, the color image is detected through the YOLO-Banana neural network to obtain two-dimensional information about the banana bunches and stalks. Then, the three-dimensional coordinates of the central point of the banana stalk are calculated according to the depth information, and the banana bunch size is obtained based on the depth information of the central point. Finally, the effective pixel ratio of the banana bunch is presented, and the banana bunch weight estimation model is statistically analyzed. Thus, the weight estimation of the banana bunch is obtained through the bunch size and the effective pixel ratio. The R2 value between the estimated weight and the actual measured value is 0.8947, the RMSE is 1.4102 kg, and the average localization error of the central point of the banana stalk is 22.875 mm. The results show that the proposed method can provide bunch size and weight estimation for the intelligent management of banana orchards, along with localization information for banana-harvesting robots.

This paper presents an intelligent system for the dynamic estimation of sheep body weight (BW). The methodology used to estimate body weight is based on measuring seven biometric parameters: height at withers, rump height, body length, body diagonal length, total body length, semicircumference of the abdomen, and semicircumference of the girth. A biometric parameter acquisition system was developed using a Kinect as a sensor. The results were contrasted with measurements obtained manually with a flexometer. The comparison gives an average root mean square error (RMSE) of 9.91 and a mean R2 of 0.81. Subsequently, the parameters were used as input in a back-propagation artificial neural network. Performance tests were performed with different combinations to make the best choice of architecture. In this way, an intelligent body weight estimation system was obtained from biometric parameters, with a 5.8% RMSE in the weight estimations for the best architecture. This approach represents an innovative, feasible, and economical alternative to contribute to decision-making in livestock production systems.

Pig point cloud data can be used to digitally reconstruct surface features, calculate pig body volume and estimate pig body weight. Volume, as a pig novel phenotype feature, has the following functions: (a) It can be used to estimate livestock weight based on its high correlation with body weight. (b) The volume proportion of various body parts (such as head, legs, etc.) can be obtained through point cloud segmentation, and the new phenotype information can be utilized for breeding pigs with smaller head volumes and stouter legs. However, as the pig point cloud has an irregular shape and may be partially missing, it is difficult to form a closed loop surface for volume calculation. Considering the better water tightness of Poisson reconstruction, this article adopts an improved Poisson reconstruction algorithm to reconstruct pig body point clouds, making the reconstruction results smoother, more continuous, and more complete. In the present study, standard shape point clouds, a known-volume Stanford rabbit standard model, a measured volume piglet model, and 479 sets of pig point cloud data with known body weight were adopted to confirm the accuracy and reliability of the improved Poisson reconstruction and volume calculation algorithm. Among them, the relative error was 4% in the piglet model volume result. The average absolute error was 2.664 kg in the weight estimation obtained from pig volume by collecting pig point clouds, and the average relative error was 2.478%. Concurrently, it was determined that the correlation coefficient between pig body volume and pig body weight was 0.95.

Background and Objectives: Camel breeding is one of the sources of income for people on the edge of arid and desert regions across various parts of the world. Traits associated with camel growth, including birth weight, weaning weight, daily weight gain, and oneyear-old weight, are considered as the principal economic attributes for camel owners. To manage and enhance the genetic and phenotypic values of these traits, animal weight recording is imperative. Recording the weight of camels, particularly due to their restless temperament and substantial size, presents numerous challenges. The utilization of effective mathematical methodologies can substantially mitigate this issue. Various approved efficient mathematical methods have been proposed to predict the weight of camels based on their body dimension, and their efficacy has been proven. Therefore, the present study was conducted to compare the effectiveness of principal component analysis and multiple regression in estimating the weight of fattening camels based on their body dimensions. Materials and Methods: In order to compare the efficiency of principal components analysis and multiple regression in estimating the weight of fattening camels based on body dimensions, the records of 220 fattening camels at the Bafgh station in Yazd were used. For this purpose, newborn camels were fed for a 9-month period using standard diets. During the period, each of the fattening camels was weighed and different body sizes including body length (BL), whither height (WH), breast girth (BG), abdomen width (AW), hump height to the ground (HH), muzzle girth (MG), neck length (NEL), whither to pin length (WPL), tail length (TL), pelvic width (PW), abdomen to hump height (ABH) and the head length (HL) were measured. The body dimensions of the camels were recorded using a tape measure, and their body weight was measured using a scale. Subsequently, the data were analyzed using the principal component analysis and multiple regression methods. In order to fit the predictive models, the body weight of camels was introduced as the dependent variable, and the body size of camels was introduced as the independent variable. The analysis of regression models was performed using both one-variable and multivariable linear models, and the best models were selected to estimate the weight of camels based on their body dimensions through the stepwise method. The performance of the above models was evaluated by comparing their coefficients of determination (R2). Results: According to the results, the correlations between the body weight of camels and their different body dimensions, including BL, WH, BG, AW, HH, MG, NEL, WPL, TL, PW,ABH, and HL, were 0.93, 0.89, 0.89, 0.89, 0.94, 0.73, 0.89, 0.90, 0.80, 0.85, 0.89, and 0.79, respectively. The results showed that, among the six multiple regression models fitted to estimate the weight of fattening camels, model No. 6 - utilizing head length, body length, breast girth, neck length, muzzle girth, and whither height as predictive variables - had the lowest error (12.06) and the highest accuracy (0.92) compared to the other models. Additionally, the results showed that utilizing the first and second principal components, along with both of them in the model, could explain 82.1%, 3.73% and 85% of the variance in body weight, respectively. The weight of fattening camels was determined using principal component analysis with an accuracy of 0.93 and an error of 11.54. Conclusion: The results of the present study showed that in order to estimate the weight of fattening camels, the use of principal component analysis, in addition to simplifying predictive models, has higher efficiency and accuracy as well as less error compared to multiple regression, and this method can be a suitable alternative to the multiple regression method in predicting the weight of camels. [ABSTRACT FROM AUTHOR]

Automatic weight detection is an essential step in the factory production of Pleurotus eryngii. In this study, a data set containing 1154 Pleurotus eryngii images was created, and then machine vision technology was used to extract eight two-dimensional features from the images. Because the fruiting bodies of Pleurotus eryngii have different shapes, these features were less correlated with weight. This paper proposed a multidimensional feature derivation method and an Attention-Based CNN model to solve this problem. This study aimed to realize the traditional feature screening task by deep learning algorithms and built an estimation model. Compared with different regression algorithms, the R2, RMSE, MAE, and MAPE of the Attention-Based CNN were 0.971, 7.77, 5.69, and 5.87%, respectively, and showed the best performance. Therefore, it can be used as an accurate, objective, and effective method for automatic weight measurements of Pleurotus eryngii. [ABSTRACT FROM AUTHOR]

Background: Weight estimation is required to enable dose calculations for weight-based drugs administered during emergency care. The accuracy of the estimation will determine the accuracy of the administered dose. This is an important matter of patient safety. The objective of this systematic review was to collect, review, evaluate, and create a synthesis of the current literature focusing on the accuracy of weight estimation in the prehospital environment. Methods: This systematic review followed the PRISMA guidelines. Studies were identified and included if they were peer reviewed, full length, published in English, and contained original data. Studies utilizing any form of weight estimation methodology in the prehospital setting (in children or adults) were included. Data on the quality of the studies and accuracy of the weight estimation systems were extracted. Common themes were also identified. Results: Twenty-five studies met the inclusion criteria, with only nine studies (36.0%) containing useful weight estimation accuracy data. The overall quality of the studies was poor. The Broselow tape and paramedic estimates were the most studied methods of weight estimation, but there was insufficient evidence to support conclusions about accuracy. The major themes identified included the importance of accurate weight estimation and drug dosing as critical matters of patient safety, and the need for training to ensure these processes are performed accurately. Conclusions: There were limited robust data identified on the accuracy of different weight estimation methods used in the prehospital setting. Future high-quality clinical research in this area is of critical importance to ensure patient safety in the prehospital environment. [ABSTRACT FROM AUTHOR]

This study documents a cost-effective strategy for managing Malayan sun bears (Helarctos malayanus) in the captive, focusing on their weight at the National Wildlife Rescue Centre (NWRC), Perak, Malaysia. The weight and development of captive bears' average aged of two months were assessed using data collected over 18 months. Asymptotic bear weight was estimated using the von Bertalanffy equation. The growth curves significantly differed between male and female bears, with males growing much larger than females from early to later growth stages. This study has determined three types of age classes of bears, where the age of adult Malayan sun bears are fully grown at 11 years and six months for males and eight years and nine months for females. Sub-adult bears ranged between one and six years old for males and one and four years for females, while cubs ranged from zero to one year old. Furthermore, the ideal weight for captive Malayan sun bears by age was also successfully determined. Such information is important for the management of this species in captivity. Ensuring the correct weight and age stage, among other criteria, could be useful for successfully releasing rehabilitated sun bears into their natural habitats. [ABSTRACT FROM AUTHOR]

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• This study determines and compares the accuracies of various weight estimation methods in children with and without known chronic diseases such as Sickle Cell Disease and Heart Diseases. • The study tests some new weight estimation methods in Ghanaian children and shows that these methods are highly accurate among Ghanaian children with and without chronic disease. The performance of various weight estimation methods in children with sickle cell disease (SCD) and heart disease (HD) has not been studied. We aimed to determine and compare the accuracies of the Broselow, Mercy, PAWPER XL and PAWPER XL-MAC tapes in Ghanaian children with no known chronic diseases (controls), SCD and HD. We prospectively recruited 631 children (199 with HD, 209 SCD and 223 controls) from the Komfo Anokye Teaching Hospital (KATH). Their weights were estimated using the Broselow, Mercy, PAWPER XL and PAWPER XL-MAC tapes. These estimated weights were compared to measured weight using mean percentage error (MPE), the proportion of weight estimates within ±10% (P10) and ±20% (P20) of measured weight. Bland-Altman limits of agreement (LOA) were determined to assess the precision of weight estimation and agreement with measured weight. The PAWPER XL, Mercy and PAWPER XL-MAC were the most accurate in all groups of children studied. All methods except the Broselow tape (BT), which performed best in the control group, had their best performance among children with SCD with negligible critical error rates (proportion of children with weight estimates > 20% of their actual weight). The P20 in the various groups of children using the BT were 88.36%, 80.21% and 51.10% respectively in the control, SCD and HD groups. The Mercy, PAWPER XL and PAWPER XL MAC tapes were generally above 90% in all groups. The Mercy, PAWPER XL and PAWPER XL-MAC tapes performed significantly better than the BT in all groups of children studied. These methods of weight estimation performed best in children with SCD with very little critical error. [ABSTRACT FROM AUTHOR]

Manual measurement of live fish is stressful and may cause injuries or post-release mortality. Therefore, indirect measurement based on image analysis should be developed. In this study, 150 Nile tilapia samples of three different size ranges (0.5-1 g, 20-30 g, and 40-60 g·fish-1) were collected. Each fish was photographed five times from above while freely swimming, and then weighed. Data from 1,500 images (10 images of each fish were analyzed: 5 for whole body and 5 without fins and tail) were manually segmented to extract the view area (V). Based on an 80-20 split test, the data were divided into two sets: training data (120 fish; 1,200 images) and validation data (30 fish; 300 images). The results showed that fish body weight (W) fitted with V without fins and tail achieved a higher coefficient of determination (r2) than whole body. The linear regression model was chosen as the best fit for W estimation based on r2 (0.922-0.958) and several error analyses: root mean square error (RMSE; 1.02±0.86 g), mean absolute error (MAE; 0.90±0.82 g), mean absolute relative error (MARE; 4.57± 4.11%), maximum absolute error (MXAE; 1.76±1.36 g), and maximum relative error (MXRE; 0.12± 0.10%). Our results indicated that utilizing a linear model was ideal and easy to apply. Furthermore, there is no suffering or weight loss associated with this procedure, since it is not necessary to harvest the fish as with traditional methods. This suggests that the findings of this study can be utilized in a subsequent phase to estimate the weight of freely moving fish, and we also favor incorporating our results with unmanned aerial vehicles (UAVs). Furthermore, Artificial Intelligence (AI) will be employed to identify models capable of autonomous operation. [ABSTRACT FROM AUTHOR]

In the U.S., peanut farmers receive premium prices for crops with high seed grades. One component of seed grade is the proportion of seed weight to that of pod hulls and other matter. Seed weight and size are also important traits for food processors. Current methods for evaluating peanut seed grade require the opening of the pod and are time-consuming and labor-intensive. In this study, a non-destructive and efficient method to determine peanut seed weights was investigated. X-ray images of a total of 513 peanut pods from three commercial cultivars, each representing three market types, were taken using a soft X-ray imaging system. The region of interest of each image, the seeds, was extracted two ways, manually and with a differential evolution segmentation algorithm. The comprehensive attenuation index (CAI) value was calculated from the segmented regions of interest. Lastly, linear regression models were established between peanut seed weights and the CAI. The results demonstrated that the X-ray imaging technology, coupled with the differential evolution segmentation algorithm, may be used to estimate seed weights efficiently from intact peanut pods. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of weight estimation and secure control for discrete stochastic distribution control (SDC) systems under sparse sensor attacks. Firstly, a Luenberger observer is designed for the linear SDC systems to perform the weight estimation under sparse sensor attacks. Then, a generalized proportional-integral (PI) tracking control strategy is proposed for the linear B-spline model. Furthermore, the tracking problem for output probability density functions (PDFs) is implemented, and the designed controller ensures that the closed-loop system is stable. Finally, the simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Automated body weight (BW) estimation is an important indicator to reflect the automation level of breeding, which can effectively reduce the damage to animals in the breeding process. In order to manage meat rabbits accurately, reduce the frequency of manual intervention, and improve the intelligent of meat rabbit breeding, this study constructed a meat rabbit weight estimation system to replace manual weighing. The system consists of a meat rabbit image acquisition robot and a weight estimation model. The robot stops at each cage in turn and takes a top view of the rabbit through an RGB camera. The images from the robot are automatically processed in the weight estimation model, which consists of the meat rabbit segmentation network based on improved Mask RCNN and the BW fitting network. Attention mechanism, PointRend algorithm, and improved activation function are proposed to improve the performance of Mask RCNN. Six morphological parameters (relative projected area, contour perimeter, body length, body width, skeleton length, and curvature) are extracted from the obtained mask, and are sent into the BW fitting network based on SVR-SSA-BPNN. The experiment shows that the system achieves a 4.3% relative error and 172.7 g average absolute error in BW estimation for 441 rabbits, while the meat rabbit segmentation network achieves a 99.1% mean average precision (mAP) and a 98.7% mean pixel accuracy (MPA). The system provides technical support for automatic BW estimation of meat rabbits in commercial breeding, which is helpful to promote precision breeding. [ABSTRACT FROM AUTHOR]

Pig farming is a crucial sector in global animal husbandry. The weight and body dimension data of pigs reflect their growth and development status, serving as vital metrics for assessing their progress. Presently, pig weight and body dimensions are predominantly measured manually, which poses challenges such as difficulties in herding, stress responses in pigs, and the control of zoonotic diseases. To address these issues, this study proposes a non-contact weight estimation and body measurement model based on point cloud data from pig backs. A depth camera was installed above a weighbridge to acquire 3D point cloud data from 258 Yorkshire–Landrace crossbred sows. We selected 200 Yorkshire–Landrace sows as the research subjects and applied point cloud filtering and denoising techniques to their three-dimensional point cloud data. Subsequently, a K-means clustering segmentation algorithm was employed to extract the point cloud corresponding to the pigs’ backs. A convolutional neural network with a multi-head attention was established for pig weight prediction and added RGB information as an additional feature. During the data processing process, we also measured the back body size information of the pigs. During the model evaluation, 58 Yorkshire–Landrace sows were specifically selected for experimental assessment. Compared to manual measurements, the weight estimation exhibited an average absolute error of 11.552 kg, average relative error of 4.812%, and root mean square error of 11.181 kg. Specifically, for the MACNN, incorporating RGB information as an additional feature resulted in a decrease of 2.469 kg in the RMSE, a decrease of 0.8% in the MAPE, and a decrease of 1.032 kg in the MAE. Measurements of shoulder width, abdominal width, and hip width yielded corresponding average relative errors of 3.144%, 3.798%, and 3.820%. In conclusion, a convolutional neural network with a multi-head attention was established for pig weight prediction, and incorporating RGB information as an additional feature method demonstrated accuracy and reliability for weight estimation and body dimension measurement.

This study aimed to investigate the role of sensorimotor expertise in evaluating relative weight of a lifted object during the observation of a sport-specific gesture, namely the deadlift. Fifty-six participants, assigned to three groups according to their experience in weight lifting, powerlifters, CrossFit® practitioners and naïve participants (controls), performed a perceptual weight judgments task. Participants observed videos showing a powerlifter executing a deadlift at the 80%, 90% and 100% of 1 repetition maximum (1RM) and answered a question about the weight of the lifted object. Participants' response accuracy and variability were evaluated. Findings showed that powerlifters were more accurate than controls. No differences appeared between powerlifter and CrossFit® practitioners, and between CrossFit® practitioners and controls. Response variability was similar in the three groups. These findings suggest that a fine sensorimotor expertise specific for the observed gesture is crucial to detect the weight of the object displayed in the observed movement, since it might allow detecting small changes in the observed movement kinematics, which we speculate are at the basis of the object weight recognition.

In emergency settings, medication dosages are primarily determined based on the child's weight; however, the healthcare personnel sometimes may have to make the first intervention without knowing the patient's weight, which may result in an underdose or overdose of the medication. We aimed to find a reliable method to estimate children's bodyweight, including the obese and malnourished. We conducted the study with children between the ages of 3 and 17 years. We measured the children's bodyweight, right foot length, and mid-upper arm circumference in centimeters during their examination. We created a concise formula through regression analysis to estimate the patients' weight based on the right foot length and mid-upper arm circumference. Finally, we compared this formula to other conventional formulae. The study included 741 patients whose average age was 8.4±3.9 (3–17) years. A high correlation was found between the patients' foot length, arm circumference, and bodyweight (R: 0.866, p<0.001 and R: 0.910, p<0.001, respectively). A single formula was created by regression analysis based on foot length and the mid-upper arm circumference without including sex and age in the calculation to make a more straightforward and faster calculation in emergency cases. The formula created may be advantageous for reliably and easily estimating the weight of children aged 3–17 of any sex and body habitus in emergency settings without special equipment. This newly developed formula may enable a low-cost optimal level of weight estimation without the need for special equipment. [ABSTRACT FROM AUTHOR]