The client is one of the largest cattle companies in the world.

The Client Requirement

During the process of letting the cattle out for grazing, the company is faced with the task of manually counting the number of cattle being let out and do the same when they come back. It has become a challenge for the client as they are dealing with a large number of cattle in the range of 1000+. The main challenge for the client is to make sure all the cattle have been accounted for without any errors as each count makes up for thousands of dollars.

How did Data Labeler Help?

The project for cattle recognition included several stages:

• Data Labeler collected the footage of the cattle in various parts of the counting process.
• Image classification was performed to classify the species of the animal.
• Annotation localization helped to place the bounding boxes over the animals.
• With annotation classification, the team was able to add species labels to each annotation.
• The labeled dataset was then used by the client to train an algorithm to identify the cattle and count them when they are let out for grazing or let in.

The Result

• The team could meet the client’s quality benchmark by achieving 100% accuracy throughout the project.
• This helped the client to train the model with precision.
• The challenge faced by the client with the traditional method of counting the cattle was overcome with the trained ML model.
• It also eliminated any chances of human error thereby achieving accuracy while counting cattle.

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