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.

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).

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.

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.

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.

Effects of different moist orange pulp inclusions in the corn grain rehydration for silage production on chemical composition, fermentation, aerobic stability, microbiological profile, and losses.

This study evaluated the effects of different moist orange pulp (MOP) inclusions in corn grain rehydration for silage production as a strategy to store and use MOP and whether these inclusions result in adequate fermentation and aerobic stability (AE) and acceptable losses. Ground corn grain and MOP were weighed separately and mixed to obtain MOP inclusions of 21%, 34%, or 42%. The control treatment was obtained with corn rehydrated with distilled water and MOP inclusion of 0%, with five laboratory silos (15-L buckets) per treatment.

Bovine respiratory disease complex associated mortality and morbidity rates in feedlot cattle from southeastern Brazil.

Introduction: A feedlot is an intensive farming system for finishing livestock. Bovine respiratory disease (BRD) is a cause of morbidity and mortality in beef cattle, especially in feedlots.

Methodology: This study investigated the morbidity and mortality of BRD in a beef cattle feedlot in southeastern Brazil using: clinical diagnoses, therapy, morbidity, and mortality.