Good livestock management can increase yields, profitability and animal welfare. Increasingly, consumers want to know where their food comes from and what conditions it was raised in. As a result, AI developers are introducing computer vision based AI models that can help improve the management of livestock.
This technology can power drone technology or be used in smart, ground based cameras systems, giving farmers a helping hand. All of these systems depend on annotated training data that teaches AI models to recognise different aspects of animal behavior.
By adding labels and semantic segmentation to images and video annotators create vital context that helps machine learning models function in the real world. Livestock management systems need to be able to understand the movements of the animals they monitor. Therefore, video annotation is particularly important in this sector.
Today's blog will look at some of the most promising use cases for AI in livestock management. We will also show how the interpolation features and the right data annotation platform can make video labeling less of a challenge for developers in this industry.
Applications for livestock management
Video annotation enables a range of essential livestock management AI applications. The following use cases can keep animals healthy and improve profitability for livestock farmers:
- Sickness and health monitoring: Identifying sickness or injury in livestock herds can allow for early intervention that improves animal welfare. It can be difficult for farm workers to monitor animals over long periods of time and across large field distances. Monitoring applications can interpret animal movements 24/7 and alert workers when signs of poor health are first appearing.
- Abnormal behavior detection: Video annotation tool allows computer vision models to analyze movement. This can be combined with other machine learning enabled capabilities to create monitoring applications that can identify unusual behavior in herd populations. This could mean spotting problematic interactions before they lead to injury or an animal that is not properly bonded with its offspring.
- Feeding rates: Monitoring feeding rates is essential for assessing the growth of livestock and identifying which animals are not eating enough or are unwell. Computer vision based AI models can learn to spot the movements that indicate that an animal is eating. These observations can lead to important metrics that allow livestock managers to keep track of how their herds are feeding and growing.
- Herd counting: Animals can get lost across large areas of grazing land, frequent herd counting is therefore essential. Herd counting AI can dramatically increase the speed and accuracy of this process, freeing up farm workers for other tasks.
Challenges of video annotation
Video annotation can be expensive for animal livestock AI companies. Workers need to label thousands of individual frames in order to create annotated video datasets. Engineers and senior leaders can often be distracted by the management burden created by video annotation.
Interpolation features can help
Object interpolation can help to streamline video annotation by labeling video frames faster. Interpolation is a feature of annotation tools that automatically tracks things between video keyframes. For livestock AI annotators locate an animal in a frame of video with a bounding box.
They can then progress the video and locate the animal in a later frame. The interpolation algorithm then creates a box label that tracks the animal through all the intervening frames. This capability greatly reduces the amount of time it takes to annotate video data.
Effective annotation tools for interpolation
Keylabs is a platform designed by annotation experts to streamline the video labeling process. The Keylabs tool features a full suite of interpolation options presented in a user-friendly manner.
Multiple annotators can work on one piece of video footage at the same time using Keylabs. Their annotations can then be seamlessly merged together. Keylabs is an efficient choice for developers looking to create precise, powerful datasets.