| Autor: |
Cabezas J; Data Science Laboratory, University Rey Juan Carlos, 28933 Móstoles, Spain., Yubero R; Data Science Laboratory, University Rey Juan Carlos, 28933 Móstoles, Spain., Visitación B; Data Science Laboratory, University Rey Juan Carlos, 28933 Móstoles, Spain., Navarro-García J; Data Science Laboratory, University Rey Juan Carlos, 28933 Móstoles, Spain., Algar MJ; Data Science Laboratory, University Rey Juan Carlos, 28933 Móstoles, Spain., Cano EL; Data Science Laboratory, University Rey Juan Carlos, 28933 Móstoles, Spain.; Quantitative Methods and Socioeconomic Development Group, Institute for Regional Development, University of Castilla-La Mancha, 02071 Albacete, Spain., Ortega F; Data Science Laboratory, University Rey Juan Carlos, 28933 Móstoles, Spain. |
| Abstrakt: |
In this paper, a method to classify behavioural patterns of cattle on farms is presented. Animals were equipped with low-cost 3-D accelerometers and GPS sensors, embedded in a commercial device attached to the neck. Accelerometer signals were sampled at 10 Hz, and data from each axis was independently processed to extract 108 features in the time and frequency domains. A total of 238 activity patterns, corresponding to four different classes ( grazing , ruminating , laying and steady standing ), with duration ranging from few seconds to several minutes, were recorded on video and matched to accelerometer raw data to train a random forest machine learning classifier. GPS location was sampled every 5 min, to reduce battery consumption, and analysed via the k-medoids unsupervised machine learning algorithm to track location and spatial scatter of herds. Results indicate good accuracy for classification from accelerometer records, with best accuracy (0.93) for grazing . The complementary application of both methods to monitor activities of interest, such as sustainable pasture consumption in small and mid-size farms, and to detect anomalous events is also explored. Results encourage replicating the experiment in other farms, to consolidate the proposed strategy. |
