APPLICATION OF THE RANDOM FOREST ALGORITHM FOR EARLY DETECTION OF LAMENESS IN DAIRY COWS

Abstract

This study explores the application of pedometers as a tool for the early detection of lameness in dairy cattle. By continuously monitoring cattle activity through pedometer data, including step count, distance traveled, and other physical activity parameters, we aim to develop a machine learning-based system capable of identifying early signs of lameness. The research highlights the advantages of using pedometers attached to the legs of cattle, which offer more accurate data collection compared to other wearable devices.