Álvaro García
Effective beef cattle growth management relies on consistently monitoring key factors, like body weight, body condition score, and lameness. These metrics are crucial for making decisions about breeding, nutrition, and daily weight gain in cattle herds or feedlots. For example, assessing beef cattle’s maturity type often involves measuring hip height relative to age. Monitoring weight is critical because it can help us detect and predict health issues, allowing us to take timely action. Body condition score offers insights into the animal’s energy reserves and nutritional status, which, in turn, help us fine-tune feeding strategies and overall health. Additionally, identifying lameness early is vital for the animals’ well-being and prompts appropriate treatment.
Traditional methods of managing beef cattle have involved using hands-on tools and in-person observations, but these approaches have their drawbacks. Firstly, they can stress the animals. The presence of humans often disrupts cattle behavior, leading to potential stress and well-being issues, and it also puts human operators at risk of injury. Moreover, the reliability of observations can vary depending on the experience of the observer, making it less sensitive to subtle changes. Lastly, traditional methods come with their own set of challenges. Measuring body dimensions requires trained operators to use instruments like measuring sticks or hipometers, requiring cattle to remain still in containment, which can be stressful. Weighing cattle relies on expensive industrial scales and skilled labor, while BCS assessment involves a subjective manual scoring protocol, requiring assessors with knowledge of skeletal structures and fat reserves. In essence, traditional manual methods are labor-intensive, time-consuming, and subjective, which limits their usefulness, especially when dealing with large herds.
The field of precision livestock farming has ushered in significant improvements, and one promising innovation is 3D computer vision. It allows for continuous, remote, and non-invasive monitoring of individual cattle’s health and well-being in real-time. These systems enable more efficient cattle management while minimizing direct human interaction. While two-dimensional computer vision has been used to measure morphological characteristics and estimate live body weight, it has limitations in identifying critical anatomical measurements for cattle growth management. These measurements are often challenging to pinpoint in two dimensions due to their distribution on the three-dimensional surfaces of cattle. In contrast, three-dimensional systems, using cost-effective 3D cameras, have overcome many of these challenges. These 3D cameras capture the depth dimension, providing additional valuable data points, thus mitigating the limitations of 2D-based systems.
Despite these technological advancements, it’s important to note that BCS in beef cattle remains underutilized. According to the USDA, only approximately 23% of U.S. beef cattle operations incorporate BCS into their management decisions. Given the practicality and usefulness of this measurement, there is an urgent need to develop objective BCS estimation methods that do not rely on experienced scorers. Three-dimensional (3D) cameras have been successfully used to capture images for extracting features related to body condition and estimating BCS in dairy cows. Machine learning, a versatile technique that derives predictive models from available data without prior knowledge of the relationships among variables, has played a significant role in this success. Several studies have developed BCS estimation models for dairy cows by combining 3D image analysis with machine learning techniques, achieving overall accuracies of approximately 75%. While these achievements have been noteworthy in the context of dairy cattle, they have been less common in beef cattle production.
In beef cattle production settings, such as cow-calf operations and feedlots, simple and objective methods for BCS estimation would be highly valuable for cattle managers. These methods would empower farmers without specialized training to estimate BCS practically. However, automatic measurement of animal body dimensions hinges on precise localization of key points or regions within point clouds of data. Achieving this level of accuracy usually requires additional assistance and sometimes results in semi-automatic solutions with suboptimal precision.
There is a promising new approach for segmenting beef cattle point clouds, which utilizes the Bidirectional Tomographic Slice Segmentation algorithm (Jiawei Li et al. 2022). This innovative method enables the segmentation of cattle point clouds into distinct regions, including the head and neck, front trunk, middle trunk, back trunk, lower leg, and hip and tail regions, with remarkable accuracies of 89%, 91%, 94%, 95%, 92%, and 95%, respectively. Consequently, the segmentation completion rate reaches an impressive 96%, and the average segmentation accuracy stands at 92.8%. This new algorithm exhibits versatility by accurately segmenting point clouds of other cloven-hoofed livestock species. The precise localization of key regions offers opportunities for measuring body dimensions and conducting non-contact weighing, providing valuable support for various breeding needs, including health assessment, production performance evaluation, and genetic breeding assessment.
In summary, integrating 3D camera technology into beef cattle management represents a significant stride toward sustainability and efficiency in the industry. The adoption of innovative tools for continuous monitoring, accurate growth estimation, and body condition scoring is crucial to address the evolving challenges posed by a growing global population and increasing food demand. These technologies offer not only precision but also the potential to transform cattle care, leading to healthier, more productive herds and a more sustainable future for beef production.
© 2025 Dellait Knowledge Center. All Rights Reserved.
