Objective dairy cow mobility analysis and scoring system using computer vision-based keypoint detection technique from top-view 2D videos.

The objective of this study was to assess the applicability of a computer vision-based keypoint detection technique to extract mobility variables associated with mobility scores from top-view 2-dimensional (2D) videos of dairy cows. In addition, the study determined the potential of a machine learning classification model to predict mobility scores based on the newly extracted mobility variables. A data set of 256 video clips of individual cows was collected, with each clip recorded from a top-view perspective while the cows were walking.

Monitoring mammary gland development in preweaning dairy heifers using ultrasound imaging.

Existing literature on the long-term effects of nutritional manipulation on milk production is incomplete and inconsistent as animals are typically culled before lactation. In addition, longitudinal studies are rarely conducted due to high costs and constraints related to tissue sampling; therefore, additional research is necessary to better understand the effect of early nutrition and growth on subsequent growth and milk production. Thirty Holstein heifer calves were pair-fed high (H; 27% CP and 20% fat) or low (L; 22% CP and 15%) milk replacers.

SheepEye: a based-web app for real-time diagnosis of sheep anemia.

is an extremely harmful blood-feeding nematode affecting small ruminants, leading to anemia, weight loss, and, in severe cases, animal death. Traditional methods of monitoring anemia in sheep, such as regular physical examinations by veterinarians and laboratory tests, can be expensive and time-consuming. In this work, we propose an anemia monitoring system that uses a web-based app.

Empowering informed choices: How computer vision can assists consumers in making decisions about meat quality.

Consumers often find it challenging to assess meat sensory quality, influenced by tenderness and intramuscular fat (IMF). This study aims to develop a computer vision system (CVS) using smartphone images to classify beef and pork steak tenderness (1), predicting shear force (SF) and IMF content (2), and performing a comparative evaluation between consumer assessments and the method's output (3). The dataset consisted of 924 beef and 514 pork steaks (one image per steak).

Graduate Student Literature Review: Mammary gland development in dairy cattle-Quantifying growth and development.

Mammary gland development research in dairy cattle has improved tremendously over the years, ranging from palpation to methods such as DNA and RNA sequencing, histological imaging, and medical imaging. Despite these advancements, limited evidence relating milk production with early mammary development exists due to incomplete and conflicting data. Further, data are typically not collected longitudinally in the same animals allowing for repeated measures analysis.

Ultrasound-assisted minimally invasive technique for tissue collection from bovine mammary glands.

Background And Aims: The objective of this case report was to describe an ultrasound-guided, minimally invasive method for longitudinal mammary gland tissue collection from the bovine species.

Materials And Methods: Biopsies were performed on 14 8-week-old calves and 113 10-week-old calves. A subset of 36 animals had repeated mammary gland biopsies through the first lactation.

Predicting respiration rate in unrestrained dairy cows using image analysis and fast Fourier transform.

Respiratory rate (RR) is commonly employed for identifying animals experiencing heat-stress conditions and respiratory diseases. Recent advancements in computer vision algorithms have enabled the estimation of the RR in dairy cows through image-based approaches, with a primary focus on standing positions, thermal imaging, and deep learning techniques. In this study, our objective was to develop a system capable of accurately predicting the RR of lying Holstein cows under unrestrained conditions using red, green, and blue (RGB) and infrared (IR) night vision images.

Near-infrared spectroscopy analysis of blood plasma for predicting nonesterified fatty acid concentrations in dairy cows.

During the transition period, dairy cows are often exposed to negative energy balance (NEB), leading to lipid mobilization from adipose tissue into nonesterified fatty acids (NEFA), a common indicator of heightened illness risk. This study aimed to use blood near-infrared (NIR) spectra data to classify NEB into high or low categories, based on early-lactation cow NEFA thresholds. We collected a total of 186 plasma samples from 100 Holstein cows.

Actively ventilating calf hutches using solar-powered fans: Effects on hutch microclimate and calf thermoregulation.

Although active ventilation via fans is an effective and widely adopted heat abatement method for use with adult dairy cattle, it has yet to be investigated in outdoor hutch-housed dairy calves despite most US calves being raised in such systems. We investigated a solar-powered fan system for outdoor calf hutches and its effect on hutch microclimate and calf thermoregulation. During summer, a 3 × 3 Latin square was replicated 4 times (n = 12 preweaning heifers) with 4-d exposure periods to minimally (CON; rear windows closed), passively (PASS; rear windows opened), or actively (ACT; solar-powered fan, activated at dry bulb temperature [T] > 21°C) ventilated hutch systems.

Image analysis to automatically classify anemia based on Famacha score in sheep using ocular conjunctiva images.

is the most pathogenic blood-feeding parasitic in sheep, causing anemia and consequently changes in the color of the ocular conjunctiva, from the deep red of healthy sheep to shades of pink to practically white of non-healthy sheep. In this context, the Famacha method has been created for detecting sheep unable to cope with the infection by , through visual assessment of ocular conjunctiva coloration. Thus, the objectives of this study were (1) to extract ocular conjunctiva image features to automatically classify Famacha score and compare two classification models (multinomial logistic regression-MLR and random forest-RF) and (2) to evaluate the applicability of the best classification model on three sheep farms.

Mercury Contamination as an Indicator of Fish Species' Trophic Position in the Middle Araguaia River, Brazil.

This study evaluates the use of mercury (Hg) concentrations in fish muscle tissue to determine a species' trophic position (TP) in its environment. A campaign conducted in 2019 along 375 km in the middle Araguaia River basin, Brazil, resulted in 239 organisms from 20 species collected. The highest total mercury (THg) concentrations were found in (6.

Predictive models for disease detection in group-housed preweaning dairy calves using data collected from automated milk feeders.

In the United States, dairy calves are typically housed individually due to the perception of reduced risk of spreading infectious diseases between calves and the ability to monitor health on an individual calf basis. However, automated milk feeders (AMF) can provide individual monitoring of group-housed calves while allowing them to express more natural feeding behaviors and interact with each other. Research has shown that feeding behaviors recorded by AMF can be a helpful screening tool for detecting disease in dairy calves.

Using pseudo-labeling to improve performance of deep neural networks for animal identification.

Contemporary approaches for animal identification use deep learning techniques to recognize coat color patterns and identify individual animals in a herd. However, deep learning algorithms usually require a large number of labeled images to achieve satisfactory performance, which creates the need to manually label all images when automated methods are not available. In this study, we evaluated the potential of a semi-supervised learning technique called pseudo-labeling to improve the predictive performance of deep neural networks trained to identify Holstein cows using labeled training sets of varied sizes and a larger unlabeled dataset.

Exploring associations among morphometric measurements, genetic group of sire, and performance of beef on dairy calves.

Sire selection for beef on dairy crosses plays an important role in livestock systems as it may affect future performance and carcass traits of growing and finishing crossbred cattle. The phenotypic variation found in beef on dairy crosses has raised concerns from meat packers due to animals with dairy-type carcass characteristics. The use of morphometric measurements may help to understand the phenotypic structures of sire progeny for selecting animals with greater performance.

Land use impact on mercury in sediments and macrophytes from a natural lake in the Brazilian savanna.

Freshwater ecosystems are essential to human and wildlife survival. They harbor a wide biodiversity that contributes to ecosystem services. In the ecosystem of the Brazilian Savanna, anthropic activities related to environmental pollution that includes mercury (Hg) is of concern.

Longitudinal analysis of bovine mammary gland development.

Many studies on bovine mammary glands focus on one stage of development. Often missing in those studies are repeated measures of development from the same animals. As milk production is directly affected by amount of parenchymal tissue within the udder, understanding mammary gland growth along with visualization of its structures during development is essential.

Use of Biometric Images to Predict Body Weight and Hot Carcass Weight of Nellore Cattle.

The objective of this study was to evaluate different methods of predicting body weight (BW) and hot carcass weight (HCW) from biometric measurements obtained through three-dimensional images of Nellore cattle. We collected BW and HCW of 1350 male Nellore cattle (bulls and steers) from four different experiments. Three-dimensional images of each animal were obtained using the Kinect model 1473 sensor (Microsoft Corporation, Redmond, WA, USA).

Increasing the prepartum dose of rumen-protected choline: Effects on milk production and metabolism in high-producing Holstein dairy cows.

Peripartum rumen-protected choline (RPC) supplementation is beneficial for cow health and production, yet the optimal dose is unknown. In vivo and in vitro supplementation of choline modulates hepatic lipid, glucose, and methyl donor metabolism. The objective of this experiment was to determine the effects of increasing the dose of prepartum RPC supplementation on milk production and blood biomarkers.

Mercury Exposure in Women of Reproductive Age in Rondônia State, Amazon Region, Brazil.

Environmental contamination by mercury (Hg) is a problem of global scale that affects human health. This study's aim was to evaluate Hg exposure among women of reproductive age residing in the Madeira River basin, in the State of Rondônia, Brazilian Amazon. This longitudinal cohort study used linear regression models to assess the effects on Hg levels of breastfeeding duration at 6 months, and of breastfeeding duration and number of new children at 2-year and 5-year.

Our evolved understanding of the human health risks of mercury.

Mercury (Hg) is a chemical of health concern worldwide that is now being acted upon through the Minamata Convention. Operationalizing the Convention and tracking its effectiveness requires empathy of the diversity and variation of mercury exposure and risk in populations worldwide. As part of the health plenary for the 15th International Conference on Mercury as a Global Pollutant (ICMGP), this review paper details how scientific understandings have evolved over time, from tragic poisoning events in the mid-twentieth century to important epidemiological studies in the late-twentieth century in the Seychelles and Faroe Islands, the Arctic and Amazon.

Feeding behaviors collected from automated milk feeders were associated with disease in group-housed dairy calves in the Upper Midwest United States.

Automated milk feeders (AMF) are an attractive option for producers interested in adopting practices that offer greater behavioral freedom for calves and can potentially improve labor management. These feeders give farmers the opportunity to have a more flexible labor schedule and more efficiently feed group-housed calves. However, housing calves in group systems can pose challenges for monitoring calf health on an individual basis, potentially leading to increased morbidity and mortality.

Assessing optimal frequency for image acquisition in computer vision systems developed to monitor feeding behavior of group-housed Holstein heifers.

Computer vision systems have emerged as a potential tool to monitor the behavior of livestock animals. Such high-throughput systems can generate massive redundant data sets for training and inference, which can lead to higher computational and economic costs. The objectives of this study were (1) to develop a computer vision system to individually monitor detailed feeding behaviors of group-housed dairy heifers, and (2) to determine the optimal frequency of image acquisition to perform inference with minimal effect on feeding behavior prediction quality.