Conscientious livestock management can increase yields, profitability and animal welfare. At a time when more and more consumers are concerned about the provenance of their food and conditions in which it was raised, computer vision based AI models are being used to manage and improve the management of livestock.
Whether incorporated into drone technology or in ground based cameras systems livestock management models need to be trained with annotated data. By adding labels and semantic segmentation to images and video annotators create vital context that helps machine learning models function in the real world. Video annotation is particularly important in this sector because it allows computer vision systems to interpret and analyze movement.
This blog will look at the function of video training data annotation in the livestock management AI industry, highlight some of the exciting use cases that it enables, and identify the best way to access video annotation support.
Developing a video annotation operation
Video annotation requires a significant investment of resources and labour hours. Annotating each individual frame in a piece of video footage can take an individual annotator potentially hundreds of hours. Labeling can be accelerated with the use of techniques like object interpolation, however, the task of assembling and managing a video annotation team remains prohibitive for most developers.
Hiring, training, and managing a video annotation operation can be a distraction for busy engineers and executives. And doing large scale annotation in-house can mean a lack of flexibility when data needs change.
Applications for livestock management
Despite the challenges of video annotation it is vital for many livestock management applications. The important use cases supported by video annotation include:
- 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 behaviour detection: Video annotation 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 behaviour 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.
Finding the right video annotation support
As discussed above video annotation can be a challenge for many AI companies. Annotation providers with expertise in video labeling, like Keymakr, can support developers with cost-effective service options:
- Semantic segmentation: This commonly deployed annotation technique places each pixel in an image or frame into a particular label class. Keymakr’s experienced in-house teams can create semantically separated video data whilst maintaining precision.
- Management options: Keymakr’s annotation platform deployed unique project management capabilities to allow managers to view individual’s performance and assign tasks based on capacity.
- Cost-effectiveness: Keymakr’s scalable video annotation options allow AI companies to access annotated data when they need it and reduce costs when necessary.
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