Journal
SENSORS
Volume 19, Issue 23, Pages -Publisher
MDPI
DOI: 10.3390/s19235334
Keywords
livestock; behavior classification; GPS; accelerometer; Random Forest; Kappa coefficient; dryland
Funding
- International Platform for Dryland Research Education
- Tottori University and Marginal Region Agriculture Project of Tottori University
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Different livestock behaviors have distinct effects on grassland degradation. However, because direct observation of livestock behavior is time- and labor-intensive, an automated methodology to classify livestock behavior according to animal position and posture is necessary. We applied the Random Forest algorithm to predict livestock behaviors in the Horqin Sand Land by using Global Positioning System (GPS) and tri-axis accelerometer data and then confirmed the results through field observations. The overall accuracy of GPS models was 85% to 90% when the time interval was greater than 300-800 s, which was approximated to the tri-axis model (96%) and GPS-tri models (96%). In the GPS model, the linear backward or forward distance were the most important determinants of behavior classification, and nongrazing was less than 30% when livestock travelled more than 30-50 m over a 5-min interval. For the tri-axis accelerometer model, the anteroposterior acceleration (-3 m/s(2)) of neck movement was the most accurate determinant of livestock behavior classification. Using instantaneous acceleration of livestock body movement more precisely classified livestock behaviors than did GPS location-based distance metrics. When a tri-axis model is unavailable, GPS models will yield sufficiently reliable classification accuracy when an appropriate time interval is defined.
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