Álvaro García
The pursuit of high-quality, nutritious meat has motivated meat processors to seek faster, more accurate, and cost-effective assessment methods. Consumers now prioritize sensory attributes and nutritional value, making carcass evaluation more critical beyond mere pricing. However, traditional grading methods, while effective, are time-consuming, costly, and intrusive, driving the exploration of non-destructive, precise technologies. Recent advancements in portability, accuracy, and machine learning have spurred research in this field. Carcass classification systems are crucial for understanding livestock products and market trends, but their reliance on manual grading presents challenges in delivering precise meat yield predictions while maintaining hygiene and production speed. This need has accelerated the development of real-time, noninvasive technologies using sensors to predict carcass composition and meat quality, enabling accurate forecasts of detailed product attributes.
Technologies used to predict carcass composition
Computed Tomography (CT): This technology accurately measures lean, fat, and bone in cattle carcasses by distinguishing tissues in three dimensions, showing potential for precise body composition predictions.
Dual-Energy X-ray Absorptiometry (DXA): Offering cost-effectiveness and reduced radiation exposure, DXA efficiently predicts carcass composition, aiding precision nutrition and detailed tissue analysis.
Ultrasound (US): Despite encountering challenges in cattle assessment, ultrasound explores primal cut estimation and enhances meat quality through high-intensity treatments, providing real-time, non-invasive assessments.
Computer vision systems (CVS) and 3D vision tech: They employ algorithms and machine learning to accurately evaluate meat quality, fat distribution, and composition. Laser cameras and scanning systems non-invasively measure cattle dimensions for growth monitoring and health assessment, crucial for estimating carcass composition and predicting meat quality traits such as tenderness and marbling.
These advancing technologies hold vast potential in the meat industry, ensuring superior products and refining processing methods. Predicting carcass composition in live animals is crucial for farmers, allowing them to gauge readiness for market and optimize feeding strategies. While methods like CT scanning and Dual-Energy X-ray Absorptiometry (DXA) are typically post-mortem, ultrasound and 3D vision technologies offer real-time, non-invasive assessments, making them more suitable for live animals.
Ultrasound and 3D vision systems each offer unique advantages in practical on-field applications for live animals. Ultrasound technology stands out for its non-invasive nature, allowing real-time assessments without harm to the animals. Its portability makes it suitable for on-field use, and it can be easily maneuvered to assess different body parts. Additionally, it provides immediate data, enabling quick decision-making on the farm regarding feeding strategies and market readiness.
In contrast, 3D vision systems provide a more comprehensive evaluation. These systems offer intricate three-dimensional data, enabling precise estimations of carcass composition, meat yield, and various quality parameters. They excel in accurately predicting meat quality attributes such as tenderness, marbling, and color consistency, ensuring the production of high-quality meat products. Integrating laser cameras and 3D scanning systems allows for non-invasive measurement of cattle body dimensions, aiding in growth monitoring and health assessment.
Technologies used to predict live animal composition
The development of technologies to predict carcass traits in live animals before harvest could revolutionize animal grouping and management decisions for farmers, potentially commanding higher prices in the market. Ultrasound can record carcass traits in live animals, but its implementation requires animal restraint, specialized personnel, and equipment, leading to stress for the animals and increased labor and costs for producers. To address these limitations, researchers have proposed computer vision-based methods to evaluate body composition and weight using 3D images in the beef industry, offering safer, reasonably accurate, and less stressful alternatives for animals. However, these methods primarily focus on estimating body weight, backfat thickness, fat percentage, and muscle depth, with limited focus on predicting specific traits such as ribeye area or circularity in live animals.
Many of these studies employ computer vision techniques for biometric body measurements like volume, area, length, and width to predict desired outcomes such as body weight or composition. However, research has indicated shortcomings in accurately predicting muscle depth, backfat thickness, and body fat percentage using these methods. Some studies explore deep learning strategies, like employing Convolutional Neural Networks (CNNs), for predicting body weight with acceptable precision, yet they still struggle with muscle depth and backfat thickness estimation. Specifically, CNN are tailored for visual data analysis, they process images, making them ideal for tasks like analyzing 3D body surface images in livestock. Consisting of convolutional, pooling, and fully connected layers, CNNs excel in extracting image features crucial for estimating carcass traits such as ribeye area and circularity. Circularity refers to a geometric measure used to describe the shape of the ribeye, quantifying how closely the shape of the ribeye resembles a perfect circle.
Studies with live animals
In a recent study by Caffarini et al. (2022), advancements in predicting carcass traits in live animals were explored, specifically focusing on estimating ribeye area and circularity. This investigation employed a deep learning framework using 3D body surface images. The methodology incorporated two neural networks: a nested Pyramid Scene Parsing Network (nPSPNet) for feature extraction through image segmentation and a Convolutional Neural Network (CNN) for predicting ribeye area and circularity based on the extracted features by the nPSPNet. Image segmentation carried out by the nPSPNet involves partitioning the image into distinct sections, aiming to identify and classify various components within the image. This process aids in extracting crucial features necessary for accurately predicting traits like ribeye area and circularity. The study assessed different neural network architectures to gauge their performance, feature space utility, interpretability, and training times to determine the most effective model for estimating ribeye area and circularity. Depth images were favored over RGB or grayscale images due to their ability to measure each pixel’s distance from the camera, providing more detailed information about animal size and shape.
This experiment showcased the potential of deep learning in automating ribeye area and circularity measurements from 3D livestock images, offering efficient and scalable assessment methods. It benefits beef crosses categorization based on desired ribeye traits and aids in monitoring these traits in various cattle breeds, potentially optimizing animal performance and carcass quality early on. Using body weight alone for grouping may not ensure consistent carcass traits due to body frame differences, requiring the exploration of extra traits to create better grouping strategies for feed efficiency, carcass quality, and reproduction.
Miller et al. (2019) employed 3D imaging and machine learning algorithms, particularly artificial neural networks, to forecast liveweight (LW) and carcass features in live steers and heifers. Using an automated camera system, three-dimensional images and LW data were gathered passively from these animals before slaughter, either on the farm or upon entry to the abattoir. Algorithms automatically extracted sixty potential predictor variables from these 3D images, encompassing measurements like lengths, heights, widths, areas, volumes, and ratios. These variables were then used to create predictive models for liveweight and carcass characteristics. Cold carcass weights were provided by the abattoir, and saleable meat yield, fat, and conformation grades were determined post-slaughter. The models’ performance was evaluated using R-squared (R2) values, indicating the proportion of variability in the data explained by the predictor variables. The R2 values were 0.7 for liveweight, 0.88 for cold carcass weight, and 0.72 for saleable meat yield. Moreover, the models achieved 54% and 55% accuracy (R2) in predicting fat and conformation grades, respectively. This study showcases the potential of 3D imaging combined with machine learning to forecast liveweight, saleable meat yield, and traditional carcass features in live animals. Such a system could significantly enhance beef production efficiency by autonomously monitoring finishing cattle on the farm and optimizing the timing of animal marketing.
Practical application in the field: The case for 3D cameras
The utilization of 3D cameras in livestock management extends beyond carcass traits, presenting a holistic approach to enhance animal welfare and productivity. These cameras offer multifaceted advantages, notably in determining weight, body condition score, and lameness detection. Their ability to capture detailed 3D body images facilitates accurate weight estimation by analyzing volume changes over time, providing insights crucial for optimizing feeding strategies and health management. Additionally, the technology aids in assessing body condition scores, crucial for understanding an animal’s overall health and nutritional status, contributing to more targeted and effective feeding programs. Furthermore, 3D cameras enable early detection of lameness by identifying gait irregularities and asymmetries, allowing prompt intervention to mitigate discomfort and prevent further complications. This comprehensive data collection not only enhances individual animal care but also contributes to overall herd health and productivity, showcasing the broad-ranging utility of 3D camera technology in livestock management practices.
In conclusion, the deep learning framework’s superior performance in predicting ribeye area and circularity from 3D body surface images surpasses traditional regression methods based on biometric measurements. This capability extends to potentially automating estimations of other vital metrics such as carcass yield and backfat thickness. Such advancements open avenues for extensive phenotyping in beef crosses and various cattle breeds, offering valuable tools for optimizing livestock management in commercial settings.
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