In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning
AbstractPattern classification of ingestive behavior in grazing animals has extreme importance in studies related to animal nutrition, growth and health. In this paper, a system to classify chewing patterns of ruminants in in vivo experiments is developed. The proposal is based on data collected by optical fiber Bragg grating sensors (FBG) that are processed by machine learning techniques. The FBG sensors measure the biomechanical strain during jaw movements, and a decision tree is responsible for the classification of the associated chewing pattern. In this study, patterns associated with food intake of dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior were monitored: rumination and idleness. Experimental results show that the proposed approach for pattern classification is capable of differentiating the five patterns involved in the chewing process with an overall accuracy of 94%. View Full-Text
Share & Cite This Article
Pegorini, V.; Zen Karam, L.; Pitta, C.S.R.; Cardoso, R.; da Silva, J.C.C.; Kalinowski, H.J.; Ribeiro, R.; Bertotti, F.L.; Assmann, T.S. In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning. Sensors 2015, 15, 28456-28471.
Pegorini V, Zen Karam L, Pitta CSR, Cardoso R, da Silva JCC, Kalinowski HJ, Ribeiro R, Bertotti FL, Assmann TS. In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning. Sensors. 2015; 15(11):28456-28471.Chicago/Turabian Style
Pegorini, Vinicius; Zen Karam, Leandro; Pitta, Christiano S.R.; Cardoso, Rafael; da Silva, Jean C.C.; Kalinowski, Hypolito J.; Ribeiro, Richardson; Bertotti, Fábio L.; Assmann, Tangriani S. 2015. "In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning." Sensors 15, no. 11: 28456-28471.