Intelligent farming as part of the green revolution is advancing the world of agriculture in such a way that farms become dynamic, with the overall scope being the optimization of animal production in an eco-friendly way. In this direction, this study proposes exploiting the acoustic modality for farm monitoring. Such information could be used in a stand-alone or complimentary mode to monitor the farm constantly at a great level of detail. To this end, the authors designed a scheme classifying the vocalizations produced by farm animals. More precisely, a directed acyclic graph was proposed, where each node carries out a binary classification task using hidden Markov models. The topological ordering follows a criterion derived from the Kullback-Leibler divergence. In addition, a transfer learning-based module for handling concept drifts was proposed. During the experimental phase, the authors employed a publicly available dataset including vocalizations of seven animals typically encountered in farms, where promising recognition rates were reported.
A Concept Drift-Aware DAG-Based Classification Scheme for Acoustic Monitoring of Farms / S. Ntalampiras, I. Potamitis. - In: INTERNATIONAL JOURNAL OF EMBEDDED AND REAL-TIME COMMUNICATION SYSTEMS. - ISSN 1947-3176. - 11:1(2020), pp. 4.62-4.75.
A Concept Drift-Aware DAG-Based Classification Scheme for Acoustic Monitoring of Farms
S. Ntalampiras
Primo
;
2020
Abstract
Intelligent farming as part of the green revolution is advancing the world of agriculture in such a way that farms become dynamic, with the overall scope being the optimization of animal production in an eco-friendly way. In this direction, this study proposes exploiting the acoustic modality for farm monitoring. Such information could be used in a stand-alone or complimentary mode to monitor the farm constantly at a great level of detail. To this end, the authors designed a scheme classifying the vocalizations produced by farm animals. More precisely, a directed acyclic graph was proposed, where each node carries out a binary classification task using hidden Markov models. The topological ordering follows a criterion derived from the Kullback-Leibler divergence. In addition, a transfer learning-based module for handling concept drifts was proposed. During the experimental phase, the authors employed a publicly available dataset including vocalizations of seven animals typically encountered in farms, where promising recognition rates were reported.File | Dimensione | Formato | |
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