Leading the Future of Livestock Health with Advanced AI Technology.

Revolutionizing Livestock Health: An AI-Driven Approach to Illness Detection

Services Provided

Project Type

Proof of concept

Industry

Health

Veterinary

Image diagnosis

4 weeks

The Team

Data Scientist
ML Specialists
Product designer

Elevating Cattle Health Management

A cow breeding Uruguayan entrepreneur aimed to optimize the detection of a health condition in cattle known as Lameness. This condition can greatly impact animal welfare and productivity, leading to significant economic consequences for the business.

Multiple mobile mockups showcasing high-fidelity screens of the Masterclass app.

Transforming Cattle Health with AI-Enhanced Lameness Detection

Detecting Lameness early on is challenging, requiring alternative processes that offer reliable and consistent results. By observing deviations in the cow's back, an expert can identify and quantify the problem. The goal is to develop an Artificial Intelligence (AI) model that can automatically and precisely perform this process.

The
challenge

To create an MVP solution, Rootstrap had to address risks and establish a clear technological approach. This entailed exploring different paths to solve the problem and training the algorithms accordingly. Additionally, it involved identifying the hardware and software platforms necessary for implementing the solution architecture.

Five-point sprecher system for the evaluation of lameness in a herd

Straight back

Long steps

Incurved back in movement

Short steps

Incurved back

Very short steps

Incurved back

Obvious lameness

Very evident lameness

Lying down

Addressing the needs of a cow breeding entrepreneur.

We researched and selected suitable algorithms for image segmentation using machine learning. Our goal was to detect and outline objects in images.

We evaluated both supervised and unsupervised options for automatic segmentation, involving intensive model training and data labeling. We utilized Mask R CNN with a labeled training dataset, COCO, to build an initial binary classifier for identifying healthy and unhealthy cows.