Classification of behaviour in conventional and slow-growing 2 strains of broiler chickens using tri-axial accelerometers

We applied a random forest algorithm to process accelerometer data from broiler chickens. Data from three broiler strains at a range of ages (from 25-49 days old) were used to train and test the algorithm and, unlike other studies, the algorithm was further tested on an unseen broiler strain. When tested on unseen birds from the three training broiler strains the random forest model classified behaviours with very good accuracy (92%), specificity (94%) and good sensitivity (88%) and precision (88%). With the new, unseen strain the model classified behaviours with very good accuracy (94%), sensitivity (91%), specificity (96%) and precision (91%).