Alongside crop management, the role of AI in Agriculture 2.0 extends to the management of animals and livestock. Machine learning and AI is used to innovatively monitor animal behavior, promote health and wellbeing, and increase operational efficiencies on farms.

While AI, ML, and computer vision are often discussed in reference to futuristic use cases, they can also play a pivotal role in well-established industries, such as farming. 

Computer Vision For Livestock Management

Livestock management is an ancient challenge. Many farms span thousands or even millions of hectares and are patrolled and monitored via quad bikes and other land vehicles. 

In particularly large farms, such as the 5.8 million hectares Anna Creek cattle station in Australia, monitoring livestock by air is necessary. However, helicopters cost thousands of dollars per hour of flight, making that an incredibly expensive way to monitor large areas. 

In response, unmanned aerial vehicles (UAVs) are being deployed to monitor livestock from the air. In addition to providing video footage to farmers and agriculturalists, drones can automatically account for livestock using computer vision. 

Drones deployed in this scenario reduce manual monitoring times by over 90%. 

Livestock Monitoring

As well as monitoring livestock numbers and movements, computer vision has been employed in monitoring erratic animal behaviors. This may correlate with illness or other issues that need to be proactively addressed to avoid risk. 

Monitoring livestock autonomously is efficient and maintains space for ethical farming, enabling farmers to keep a close eye on livestock without confining them to a small space. 

Livestock monitoring requires a blend of polygon annotation, bounding box annotation, image segmentation, and other techniques, such as key pointing. 

Creating quality training datasets enables machine learning teams to train the next generation of livestock management AI for agriculturalists, farmers, and livestock managers. 

Aya Data works with agronomists and agriculturalists - our labeling team is able to combine their expertise with our labeling skills to build powerful datasets. 

‍Services: Polygon, Bounding Box, Semantic Segmentation, Key Points‍

Domain Expertise: Domain Experience in Agriculture