A full end-to-end deep approach for detecting and classifying jaw movements from acoustic signals in grazing cattle

Author:

Ferrero Mariano,Vignolo Leandro D.ORCID,Vanrell Sebastián R.,Martinez-Rau Luciano S.ORCID,Chelotti José O.,Galli Julio R.,Giovanini Leonardo L.,Rufiner H. LeonardoORCID

Funder

Agencia Santafesina de Ciencia, Tecnología e Innovación

Nvidia

Universidad Nacional de Rosario

Consejo Nacional de Investigaciones Científicas y Técnicas

Universidad Nacional del Litoral

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference62 articles.

1. A review on the use of sensors to monitor cattle jaw movements and behavior when grazing;Andriamandroso;Biotechnol. Agron. Soc. Environ.,2016

2. Andriamandroso, A., Lebeau, F., Bindelle, J., 2015. Changes in biting characteristics recorded using the inertial measurement unit of a smartphone reflect differences in sward attributes. In: 7th Conference on Precision Livestock Farming. pp. 283–289.

3. Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI;Arrieta;Inf. Fusion,2020

4. CNN-RNN and data augmentation using deep convolutional generative adversarial network for environmental sound classification;Bahmei;IEEE Signal Process. Lett.,2022

5. Machine learning to detect posture and behavior in dairy cows: Information from an accelerometer on the animal’s left flank;Balasso;Animals,2021

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