Álvaro García
The beef industry faces a significant challenge when it comes to achieving the desired fat and conformation grades in animals destined for slaughter. A recent study conducted in England (Miller et al., 2017) shed light on this issue, revealing that more than half of the prime beef carcasses failed to reach their target conformation grades. In this context, it’s crucial to recognize that not only were 40% of these carcasses lacking the desired conformation, but an additional 15% were excessively fat. These disparities in carcass quality hold considerable implications, extending beyond the efficiency of the animals themselves to the realm of financial penalties. The consequences of such variations are particularly evident when dealing with overweight cattle, even if they fall within a different classification. Moreover, the financial impact extends to cattle that have not achieved the target weight, resulting in lower market prices. Therefore, the accurate quantification of weight holds paramount importance, driven by both immediate economic considerations and the long-term sustainability of beef operations. In essence, the ability to precisely measure and predict weight in cattle is not merely a matter of economic necessity in the present but a pivotal factor in shaping the future viability and profitability of the beef industry.
Numerous equations have been developed to anticipate carcass characteristics in live animals. These equations traditionally rely on labor-intensive, manual measurements of body dimensions, body condition, or tissue depth, frequently obtained by ultrasound scanning. However, this approach is not devoid of challenges, since it demands a significant investment of time and effort, necessitates specialized training and expertise, and can be stressful and potentially hazardous for both the animals and their handlers. With the advancement of imaging technologies and their increased affordability, the landscape of livestock assessment is undergoing a transformation. A recent study conducted by Ozkaya et al. (2016) demonstrated the remarkable capabilities of digital image analysis in accurately determining critical body measurements in cattle. These measurements encompass body length, wither height, chest depth, and hip height, with accuracy levels consistently ranging between 90% and 98%. This paradigm shift towards non-invasive and technologically-driven livestock assessment not only streamlines the process but also alleviates the burden of manual measurements. The accuracy and efficiency offered by digital image analysis holds the promise of revolutionizing the livestock industry, offering enhanced data collection, improved animal welfare, and increased productivity.
Two-dimensional (2D) imaging technologies have been extensively explored, however, the incorporation of three-dimensional (3D) imaging into livestock applications is currently on the rise. This advanced imaging approach offers a spectrum of possibilities, including the estimation of live weight and the evaluation of animal behavior. However, it’s noteworthy that the full potential of 3D imaging remains largely untapped, particularly in the concurrent prediction of both live weight and carcass characteristics in beef cattle. The adoption of 3D imaging technology, suspended strategically above the animals, opens up a broader array of predictor variables, enhancing prediction models to a new level of sophistication. This strategic positioning also yields the distinct advantage of keeping the imaging equipment safely distant from the animals, reducing the risk of damage, and ensuring ease of access for installation and maintenance. Within the domain of carcass grading, video image analysis has emerged as a valuable tool, offering increased grading consistency by eliminating the subjectivity associated with visual assessments conducted by trained graders. Nevertheless, it’s worth noting that many producers continue to rely on subjective visual evaluations of fat and condition scores, as well as manual weighing procedures. This operational inefficiency poses a substantial challenge within the beef industry. The implementation of 3D imaging technology possesses the transformative potential to revolutionize this selection process. It enables the prediction of carcass characteristics directly from live animals on the farm. This capability empowers farmers to make informed decisions and send the animals to the slaughterhouse as soon as they meet their specifications. Consequently, this leads to increased profitability for producers, enhanced carcass uniformity, and a reduced environmental footprint per unit of product produced. This environmental benefit extends to lower greenhouse gas emissions and decreased water usage.
The recent research conducted by Miller et al. in 2017 aimed to revolutionize the prediction of livestock parameters, specifically live weight, and carcass characteristics, by harnessing the power of 3D imaging technology and sophisticated machine learning algorithms, namely artificial neural networks. Their study focused on the passive collection of three-dimensional images and live weights from finishing steer and heifer beef cattle, encompassing a diverse array of breeds, at various stages—either on the farm or upon entry to the slaughterhouse.
Sixty potential predictor variables were automatically extracted from the 3D images of live animals, employing tailored algorithms. These variables encompassed a wide spectrum of measurements, including lengths, heights, widths, areas, volumes, and ratios. They served as the foundation for developing predictive models for live weight and carcass characteristics. The cold carcass weights of each animal were subsequently provided by the slaughterhouse. Furthermore, saleable meat yield and fat and conformation grades were meticulously determined for each individual carcass, accomplished through the visual image analysis in one half of the carcass.
To evaluate the performance of the prediction models, a rigorous assessment was conducted, employing key parameters such as the coefficient of determination (R2) and the root mean square error (RMSE). Notably, the models exhibited substantial predictive capabilities for live weight (R2 = 0.7, RMSE = 42), cold carcass weight (R2 = 0.88, RMSE = 14), and saleable meat yield (R2 = 0.72, RMSE = 14). Impressively, the models also showcased the ability to predict fat and conformation grades with an accuracy rate of 54% and 55% (R2), respectively. This pioneering study unveiled the potential of 3D imaging in conjunction with machine learning analytics, offering a transformative approach to predict liveweight, saleable meat yield, and traditional carcass characteristics in live animals. Beyond its technical prowess, this system introduces a groundbreaking opportunity to address a significant inefficiency within beef production enterprises. Through autonomous monitoring of fattening cattle on the farm and the strategic marketing of animals at the optimal time, it promises to enhance both productivity and profitability in the beef industry.
The beef industry faces significant challenges in achieving desired fat and conformation grades for cattle destined for slaughter. Recent research has shed light on this issue, revealing that over half of prime beef carcasses fail to meet their target conformation grades. These disparities not only affect the efficiency of cattle production but also result in financial penalties. Overweight cattle, even if classified differently, and underweight cattle both face economic consequences. Thus, accurate weight quantification is of utmost importance, impacting both immediate economic considerations and the long-term sustainability of the beef industry. Traditional methods for predicting carcass characteristics rely on labor-intensive manual measurements, which require time, expertise, and can be stressful for animals and handlers. However, advancements in imaging technologies have introduced non-invasive and technologically driven livestock assessment methods. These innovations, such as digital image analysis, offer improved accuracy and efficiency while reducing the burden of manual measurements.
Although two-dimensional imaging has been extensively explored, the incorporation of three-dimensional (3D) imaging in livestock applications, with its potential for predicting both live weight and carcass characteristics, is an emerging field. Suspended 3D imaging systems provide a wider array of predictor variables while ensuring equipment safety and accessibility. In the realm of carcass grading, video image analysis has improved grading consistency, but operational inefficiencies persist. This groundbreaking work showcases the potential of 3D imaging and machine learning to revolutionize the prediction of livestock parameters. By enhancing productivity and profitability in the beef industry, this innovative approach promises a more sustainable and efficient future for beef production, heralding a new era in livestock assessment.
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