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.

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.

Our proposed system architecture is decoupled, fault-tolerant, scalable, and enables traceability.

It allows for re-training and ensures data transparency and monitorability.

This approach resulted in a proof of concept that revolutionizes illness detection in cattle breeding using AI and image detection capabilities.