Álvaro García
Livestock body weight plays a crucial role in animal production, impacting feed intake, breeding, energy balance, and more. It serves as an essential indicator for health, disease, and genetic selection. Two methods are employed for measurement:
- Direct methods: These involve scales designed for different-sized animals but can be costly and logistically challenging, especially for smaller farms.
- Indirect methods: The simplest of these techniques use regression models to predict body weight based on morphometric measurements and image features. More advanced methods utilize various tools, including advanced imaging devices. Recent advances in computer vision and deep learning introduce abstract features for enhanced prediction, complementing traditional methods.
Computer vision
Computer Vision involves using 2D systems (e.g., RGB and thermal cameras), and 3D (e.g., depth sensors) systems offering detailed shape information. The process of predicting body weight consists of three stages.
- Detection: Determines the presence of animals in images. 3D cameras use sensor-subject distance, while 2D and thermal images rely on body contours.
- Segmentation: Identifies and extracts animal body boundaries. Manual methods use software tools, and automatic methods detect body parts.
- Feature extraction: Calculates body dimensions and characteristics, including length, area, and shape. Challenges can arise with crowded or indistinguishable animals.
Computer vision and machine learning
Combines computer vision techniques with machine learning methods for automated feature selection. It automates the image and feature selection processes and image segmentation, making it more suitable for high-throughput applications.
Vision and deep learning.
It incorporates deep learning techniques, such as convolutional neural networks (CNNs), recurrent convolutional neural networks (RCNNs/RNNs), recurrent attention models (RAMs), and CNNs with RAMs. It has shown significant improvements in body weight prediction but requires further development for precise automatic segmentation of animals in complex image backgrounds.
Researchers have been working to predict the weight of farm animals using traditional methods like linear regression, but these had limitations because the measurements were related to each other and were complex. Recently, some scientists have used advanced techniques called machine learning (ML) and deep learning (DL) to predict animal weights. They used data from 2D and 3D images. For example, in one study, a team used a special computer system and cameras to estimate the weight of cows. They took 2D images of the cows and used a computer program to measure different parts of their bodies. This method worked well and showed a high correlation between predicted and measured weights. Another study used artificial neural networks (ANN) to estimate cow weights. They improved their previous results, showing that these advanced techniques can be very accurate. In another study, researchers used DL to predict the weights of beef cattle. They collected 2D images from cameras and used DL to estimate weights. This method achieved the best performance. Using 3D images and ML algorithms to predict cattle weight and carcass characteristics was a big step forward, showing the potential of 3D imaging and ML. Finally, in one study, researchers focused on predicting the weight and daily gain in beef cattle using 3D images and different methods. The ANN approach had the best results for weight prediction. All these studies show how technology and advanced techniques can help estimate the weight of farm animals more accurately. This can be valuable for farmers and the livestock industry.
Precision livestock body weight estimation is a pivotal aspect of animal production, influencing various facets of farming. Traditional direct methods have their limitations, especially for smaller farms, due to cost and logistical challenges. However, innovative indirect methods have emerged, leveraging technology to predict animal weights more accurately. Cutting-edge approaches, such as computer vision and machine learning, have significantly improved livestock weight prediction. From the early stages of detection and segmentation to feature extraction, these methods provide precise measurements and can handle complex scenarios. Researchers are now employing advanced techniques like Machine Learning (ML) and Deep Learning (DL), using data from 2D and 3D images. These technologies have shown remarkable promise in accurately estimating animal weights. From cows to beef cattle, these methods have the potential to revolutionize livestock weight estimation, offering substantial benefits to farmers and the livestock industry.
From a practical perspective in the field, livestock producers, veterinarians, and nutritionists can reap numerous benefits from the innovative technologies discussed above. These advanced techniques, including computer vision, machine learning, and deep learning, offer more precise and efficient methods for estimating livestock body weight. This precision holds the potential to significantly impact animal production by facilitating more accurate management decisions.
Especially for smaller farms with limited resources, traditional direct methods of weighing animals can pose challenges due to their cost and logistical complexity. In contrast, indirect methods that incorporate computer vision and machine learning provide cost-effective alternatives. The utilization of 2D and 3D imaging technologies allows for non-invasive measurements, reducing stress on the animals and minimizing the need for physical contact, which is particularly advantageous for livestock health and well-being.
Accurate body weight estimation plays a crucial role in animal health and disease detection. With more precise measurements, veterinarians can identify health issues at earlier stages, enabling timely intervention and reducing the risk of disease outbreaks. Additionally, animal body weight is a critical factor in genetic selection. These advanced technologies offer accurate data for genetic improvement programs, enabling livestock producers to select animals more effectively with desirable traits.
From a nutritionist’s perspective, precise knowledge of animals’ body weight enables more exact management of feeding regimes, optimizing feed intake and energy balance. This leads to improved feed efficiency and cost savings for livestock producers. In addition to body weight estimation, computer vision systems can identify and analyze animal behaviors, providing valuable insights for assessing animal well-being and optimizing living conditions.
Moreover, the incorporation of machine learning and deep learning techniques with computer vision streamlines processes, making them more automated and suitable for high-throughput applications, which is particularly beneficial for large-scale livestock operations.
In conclusion, these technologies mark a significant advancement in animal science, offering a deeper understanding of livestock physiology and behavior. They open new avenues for research and development in the field. The integration of computer vision, machine learning, and deep learning in livestock body weight estimation holds promise for enhancing animal production, health management, and genetic selection. This technology offers a wide array of benefits, from precision and efficiency to cost-effectiveness and innovation in animal science, making it a valuable tool for livestock producers, veterinarians, and nutritionists in the industry.
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