Álvaro García
Understanding the weight of cows is crucial for various management decisions in beef cattle, encompassing aspects like their diet, care, and preparedness for sale. Traditionally, weighing scales have served as the primary method for determining their weight. However, this process can cause stress to the cows and result in significant time and financial costs. Innovative alternative to traditional scales is the computer vision system (CVS). This advanced technology operates by capturing three-dimensional (3D) images of the animals. Instead of physically measuring the cows, these images are used to predict their body weight. This approach offers several advantages, notably being stress-free for the animals and requiring less labor-intensive handling. Moreover, this noninvasive method doesn’t disrupt the cows’ routine or cause harm.
Automated and powered by sophisticated computer algorithms, the system rapidly delivers precise results. This ensures not only the well-being of the animals but also provides economic benefits by reducing stress-related issues and cutting labor costs for farmers. Within the CVS framework, several predictive approaches are utilized:
Multiple Linear Regression (MLR): Like drawing a line through points on a graph, MLR uses various image measurements to estimate cow weight.
Least Absolute Shrinkage and Selection Operator (LASSO): This method simplifies weight estimation by focusing on crucial measurements, akin to tidying up unnecessary information for better predictions.
Partial Least Squares (PLS): It organizes image data into smaller groups to make more efficient weight estimations by combining diverse measurements.
Artificial Neural Network (ANN): ANNs mimic human learning, identifying patterns in images to predict cow weight. They are proficient at recognizing complex patterns, aiding weight predictions without traditional scales.
A study conducted in 2023 evaluated the effectiveness of 3D imaging technology in forecasting beef heifer body weight (BW) of Red Angus/Simmental yearlings weighing between 282 to 440 kg. Using a 3D camera positioned 3 to 4 meters above the floor, dorsal projected volumes of heifers, excluding the head region, were estimated. Regression models then predicted BW from these volumes, calculating metabolic BW through a 0.75 power adjustment of BW. The evaluation compared estimated weights derived from 3D images with actual weights measured on scales using statistical methods. The results showcased the accuracy of estimated weights based on 3D images, indicated by a coefficient of determination (R2) of 0.89 and a Pearson correlation coefficient (r) of 0.94, reflecting a strong relationship between estimated and actual weights. Despite an average estimated error of around 3.3 kilograms, the 3D imaging method demonstrated high accuracy in estimating heifers’ weights. These findings highlight the precision of predicting BW using depth images of yearling heifers and emphasize the potential of 3D imaging technology in precisely estimating BW. Moreover, beyond predicting BW, these3D cameras accurately estimate the animals’ body condition. When combined with weight assessment, they provide a comprehensive portrayal of the animal’s nutritional status, offering a holistic approach to livestock management.
Striving for an ideal body condition
A study conducted by Cooke et al. (2021) assessed the reproductive and productive performances of Bos indicus and B. taurus beef cows based on body condition score (BCS) at the beginning of the breeding season. Cows scoring equal to or greater than 5 exhibited higher calving rates, earlier calving, and weaned heavier calves compared to those with a BCS lower than 5. Maintaining cows at a BCS of 5 or above during breeding is preferable, necessitating supplementary feed for cows scoring less than 5 at calving to improve condition, albeit it can be costly while nursing calves. A decline in body condition, especially for cows scoring 5 or less, from calving to breeding diminishes pregnancy rates. Cows scoring 7 or 8 can endure some condition loss without affecting breeding, provided it doesn’t drop below 5. Effective BCS management involves sorting cows 90 to 100 days before calving and feeding each group to achieve scores of 5 to 7 at calving. This maximizes reproductive performance while minimizing supplemental feed expenses. Supplemental feeding guided by BCS aids in evaluating cattle’s body composition. Cows scoring 5 or more facing reproductive issues may have deficiencies, diseases, genetic problems, or bull-related issues. Those scoring less than 5 might experience energy and protein intake insufficiencies, possibly related to factors like phosphorus and internal parasites.
In a commercial cow-calf program, deriving digestible energy from the farm’s forage is economically viable. While large energy supplements might not be feasible, cows manage energy deficits using body stores, supported by protein, mineral, and vitamin supplements. For purebred cattle, substituting forage energy with grain might be necessary for additional condition and market appeal. Protein supplementation benefits all cattle consuming low-quality forage, irrespective of BCS or lactation status, enhancing response efficiency more than energy supplementation. Although protein supplementation enhances performance, limitations exist. Cases where satisfactory performance isn’t feasible may require substantial grain-based supplements or improved forage utilization. Energy supplementation or grain feeding should be a last resort due to cost and adverse effects on forage utilization efficiency. Excessive protein relative to energy is an expensive and inefficient energy source. Timely energy utilization with protein supplements is often essential for proper development of replacement heifers and first-calf heifers, while mature cows typically do not require routine energy supplementation.
Incorporating 3D cameras in the farming operation
The introduction of 3D cameras for cattle body scoring represents a significant leap in livestock management. The advantages, such as minimal disturbance to the animals and removal of human bias, make these cameras invaluable for producers. Coordinating automatic scoring with key events like AI and pregnancy checks allows for precise grouping of cows, facilitating tailored feed allocation based on individual needs. As this technology is adopted, producers should consider integrating it into existing practices and keeping abreast of advancements to further enhance cattle operations’ efficiency. Utilizing 3D cameras not only streamlines body scoring but also enhances calving rates and the production of heavier calves at weaning, ultimately improving the overall success of cattle breeding programs and farm profitability. The adoption of 3D cameras for precision estimation of cattle body weights and body condition scores marks a significant leap forward in livestock management. These technological advancements not only offer accurate predictions of cattle metrics but also revolutionize the way we approach grazing livestock, reproduction, and health management. The findings from studies showcasing the efficacy of 3D imaging technology in forecasting cattle body weights and condition scores emphasize its profound potential. It streamlines processes, minimizes stress on animals during weighing procedures, and enhances overall livestock welfare while benefiting the economic interests of producers. Moreover, the integration of 3D cameras into existing cattle management practices opens doors for tailored feeding programs, improved calving rates, and the production of heavier calves at weaning. This integration optimizes cattle breeding programs and farm profitability, ensuring the industry continues to evolve towards more efficient and sustainable practices. As this technology continues to advance, it becomes increasingly essential for producers to not only embrace its integration but also stay updated on further advancements that could enhance cattle operations. By leveraging these innovations, producers can foster healthier herds, maximize productivity, and drive the cattle industry towards greater success and sustainability.
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