Body Dimension Measurements of Qinchuan Cattle with Transfer Learning from LiDAR Sensing

3D imaging and body measurement of riding horses using four scanners simultaneously

Although there have been advances in the technology for measuring horse body size with stereoscopic three-dimensional (3D) scanners, previously reported methods with a single scanner still face a significant challenge: the time necessary for scanning is too long for the horses to remain stationary. This study attempted to scan the horse simultaneously from four directions using four scanners in order to complete the scans in a short amount of time and then combine the images from the four scans on a computer into one whole image of each horse. This study also compared body measurements from the combined 3D images with those taken from conventional manual measurements. Nine riding horses were used to construct stereoscopic composite images, and the following 10 measurements were taken: height at the withers, back, and croup; chest depth; width of the chest (WCh), croup, and waist; girth circumference, cannon circumference (CaC), and body length. The same 10 measurements were taken by conventional manual methods. Relative errors ranged from -1.89% to 7.05%. The correlation coefficient between manual and 3D measurements was significant for all body measurements (P<0.01) except for WCh and CaC. A simple regression analysis of all body measurements revealed a strong correlation (P<0.001, R2=0.9994, root-mean-square error=1.612). Simultaneous scanning with four devices from four directions reduced the scanning time from 60 sec with one device to 15 sec. This made it possible to perform non-contact body measurements even on incompletely trained horses who could not remain stationary for long periods of time.

Computer-Vision-Based Sensing Technologies for Livestock Body Dimension Measurement: A Survey

Livestock's live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock.

A Comparison Study of Nipple-Areolar Complex Measurement: Light Detection and Ranging (LiDAR) Camera Versus Photometry

BACKGROUND

In breast surgery, achieving esthetic outcomes with symmetry is crucial. The nipple-areolar complex (NAC) plays a significant role in breast characteristic measurement. Various technologies have advanced measurement techniques, and light detection and ranging (LiDAR) technology using three-dimensional scanning has been introduced in engineering. Increasing effort has been exerted to integrate such technologies into the medical field. This study focused on measuring NAC using a LiDAR camera, comparing it with traditional methods, and aimed to establish the clinical utility of LiDAR for obtaining favorable esthetic results.

METHODS

A total of 44 patients, who underwent breast reconstruction surgery, and 65 NACs were enrolled. Measurements were taken (areolar width [AW], nipple width [NW] and nipple projection [NP]) using traditional methods (ruler and photometry) and LiDAR camera. To assess correlations and explore clinical implications, patient demographics and measurement values were collected.

RESULTS

NAC measurements using a periscope and LiDAR methods were compared and correlated. LiDAR measurement accuracy was found to be high, with values above 95% for AW, NW and NP. Significant positive correlations were observed between measurements obtained through both methods for all parameters. When comparing body mass index, breast volume with AW and NW with NP, significant correlations were observed. These findings demonstrate the reliability and utility of LiDAR-based measurements in NAC profile assessment and provide valuable insights into the relationship between patient demographics and NAC parameters.

CONCLUSIONS

LiDAR-based measurements are effective and can replace classical methods in NAC anthropometry, contributing to consistent and favorable esthetic outcomes in breast surgery.

LEVEL OF EVIDENCE II

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors https://www.springer.com/00266 .

Automatic Weight Prediction System for Korean Cattle Using Bayesian Ridge Algorithm on RGB-D Image

Weighting the Hanwoo (Korean cattle) is very important for Korean beef producers when selling the Hanwoo at the right time. Recently, research is being conducted on the automatic prediction of the weight of Hanwoo only through images with the achievement of research using deep learning and image recognition. In this paper, we propose a method for the automatic weight prediction of Hanwoo using the Bayesian ridge algorithm on RGB-D images. The proposed system consists of three parts: segmentation, extraction of features, and estimation of the weight of Korean cattle from a given RGB-D image. The first step is to segment the Hanwoo area from a given RGB-D image using depth information and color information, respectively, and then combine them to perform optimal segmentation. Additionally, we correct the posture using ellipse fitting on segmented body image. The second step is to extract features for weight prediction from the segmented Hanwoo image. We extracted three features: size, shape, and gradients. The third step is to find the optimal machine learning model by comparing eight types of well-known machine learning models. In this step, we compared each model with the aim of finding an efficient model that is lightweight and can be used in an embedded system in the real field. To evaluate the performance of the proposed weight prediction system, we collected 353 RGB-D images from livestock farms in Wonju, Gangwon-do in Korea. In the experimental results, random forest showed the best performance, and the Bayesian ridge model is the second best in MSE or the coefficient of determination. However, we suggest that the Bayesian ridge model is the most optimal model in the aspect of time complexity and space complexity. Finally, it is expected that the proposed system will be casually used to determine the shipping time of Hanwoo in wild farms for a portable commercial device.

Integrated Digital Health Solutions in the Management of Growth Disorders in Pediatric Patients Receiving Growth Hormone Therapy: A Retrospective Analysis

Digital health has seen rapid advancements over the last few years in helping patients and their healthcare professionals better manage treatment for a variety of illnesses, including growth hormone (GH) therapy for growth disorders in children and adolescents. For children and adolescents requiring such therapy, as well as for their parents, the treatment is longitudinal and often involves daily injections plus close progress monitoring; a sometimes daunting task when young children are involved. Here, we describe our experience in offering devices and digital health tools to support GH therapy across some 40 countries. We also discuss how this ecosystem of care has evolved over the years based on learnings and advances in technology. Finally, we offer a glimpse of future planned enhancements and directions for digital health to play a bigger role in better managing conditions treated with GH therapy, as well as model development for adherence prediction. The continued aim of these technologies is to improve clinical decision making and support for GH-treated patients, leading to better outcomes.

Intelligent Perception-Based Cattle Lameness Detection and Behaviour Recognition: A Review

Simple Summary

Cattle lameness detection as well as behaviour recognition are the two main objectives in the applications of precision livestock farming (PLF). Over the last five years, the development of smart sensors, big data, and artificial intelligence has offered more automatic tools. In this review, we discuss over 100 papers that used automated techniques to detect cattle lameness and to recognise animal behaviours. To assist researchers and policy-makers in promoting various livestock technologies for monitoring cattle welfare and productivity, we conducted a comprehensive investigation of intelligent perception for cattle lameness detection and behaviour analysis in the PLF domain. Based on the literature review, we anticipate that PLF will develop in an objective, autonomous, and real-time direction. Additionally, we suggest that further research should be dedicated to improving the data quality, modeling accuracy, and commercial availability.

Abstract

The growing world population has increased the demand for animal-sourced protein. However, animal farming productivity is faced with challenges from traditional farming practices, socioeconomic status, and climate change. In recent years, smart sensors, big data, and deep learning have been applied to animal welfare measurement and livestock farming applications, including behaviour recognition and health monitoring. In order to facilitate research in this area, this review summarises and analyses some main techniques used in smart livestock farming, focusing on those related to cattle lameness detection and behaviour recognition. In this study, more than 100 relevant papers on cattle lameness detection and behaviour recognition have been evaluated and discussed. Based on a review and a comparison of recent technologies and methods, we anticipate that intelligent perception for cattle behaviour and welfare monitoring will develop towards standardisation, a larger scale, and intelligence, combined with Internet of things (IoT) and deep learning technologies. In addition, the key challenges and opportunities of future research are also highlighted and discussed.

Body measurement of riding horses with a versatile tablet-type 3D scanning device

The measurement of various body dimensions of horses plays a significant role in quality improvement, genetic breeding, health, and soundness. There has been significant advancement in the technology for acquiring stereoscopic images with a three-dimensional (3D) scanner. This study aimed to validate the accuracy of body measurements obtained from stereoscopic images taken with a 3D scanner. We manually took the following body measurements for 8 riding horses: height at the withers, height at the back, height at the croup, chest depth, width of the chest, width of the croup, width of the waist, girth circumference, cannon circumference, and body length. Using a versatile tablet-type 3D scanning device, we captured a 3D image of each horse. Relative errors varied from −1.37% to 6.25%. The correlation coefficient between manual and 3D measurements was significant for all body measurements (P<0.01) except for width of the waist and cannon circumference. The low accuracy of cannon circumference (r=0.248) was due to effect of hair. A simple regression analysis of all body measurements revealed a strong correlation (P<0.001, R²=0.9994, root-mean-square error [RMSE]=1.522). Notable advantages of this methodology include high accuracy, good operability, non-contact, high versatility, and low cost. Further studies are required for the establishment of an accurate measurement methodology that can scan the whole body in a shorter time.

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market animals because animals that have already reached the point of slaughter represent a burden for the feedlot. There are currently two main approaches (direct and indirect) to measure the BW in livestock. Direct approaches include partial-weight or full-weight industrial scales placed in designated locations on large farms that measure passively or dynamically the weight of livestock. While these devices are very accurate, their acquisition, intended purpose and operation size, repeated calibration and maintenance costs associated with their placement in high-temperature variability, and corrosive environments are significant and beyond the affordability and sustainability limits of small and medium size farms and even of commercial operators. As a more affordable alternative to direct weighing approaches, indirect approaches have been developed based on observed or inferred relationships between biometric and morphometric measurements of livestock and their BW. Initial indirect approaches involved manual measurements of animals using measuring tapes and tubes and the use of regression equations able to correlate such measurements with BW. While such approaches have good BW prediction accuracies, they are time consuming, require trained and skilled farm laborers, and can be stressful for both animals and handlers especially when repeated daily. With the concomitant advancement of contactless electro-optical sensors (e.g., 2D, 3D, infrared cameras), computer vision (CV) technologies, and artificial intelligence fields such as machine learning (ML) and deep learning (DL), 2D and 3D images have started to be used as biometric and morphometric proxies for BW estimations. This manuscript provides a review of CV-based and ML/DL-based BW prediction methods and discusses their strengths, weaknesses, and industry applicability potential.