Machine Learning Methods and Visual Observations to Categorize Behavior of Grazing Cattle Using Accelerometer Signals
Written on May 3rd , 2024 by Ira Parsons
I recently explored how machine learning can enhance cattle behavior monitoring with wearable accelerometers. At Mississippi State University, we equipped 10 Brahman-cross steers with GPS collars that had tri-axial accelerometers and video cameras to observe behaviors like grazing, walking, and resting. Using random forest models, I found that a 10-second smoothing window boosted our classification accuracy, but cutting data below 50% reduced performance. Grazing stood out with its distinct head-down signals, making it easier to spot. We also compared in-pasture observations with collar camera footage, and I noticed the cameras captured a broader range of behaviors across larger areas. Published in Sensors (2024), our work shows how precision livestock technology can improve sustainable grazing practices, offering detailed insights into cattle behavior while easing labor demands in vast rangelands. Smart collars are truly revolutionizing animal agriculture!
Parsons, Ira Lloyd, Brandi B. Karisch, Amanda E. Stone, Stephen L. Webb, Durham A. Norman, and Garrett M. Street. 2024. “Machine Learning Methods and Visual Observations to Categorize Behavior of Grazing Cattle Using Accelerometer Signals” Sensors 24, no. 10: 3171. https://doi.org/10.3390/s24103171
