In the context of cattle farming, understanding the feeding behavior of animals is essential to ensure their welfare and maximize productivity. However, monitoring and interpret- ing the acoustic signals associated with grazing, particularly the sound events related to grass intake, pose a significant challenge. This study proposes an innovative method based on 1D convolutional neural networks to automatically classify such sound events during grazing. The approach was developed using a balanced dataset composed of 322 prehension samples and 1000 non-prehension samples, extracted from audio recordings of grazing cattle in real conditions. The results obtained show a high accuracy of 100% during the testing and validation phases of the model. However, there is concern about overfitting of the model due to the limited size of the dataset used. Consequently, future expansion of the dataset is suggested by collecting a larger and more diverse number of audio recordings to improve the generalization and robustness of the model in real cattle farming contexts.

Detecting the Number of Bite Prehension of Ggrazing Cows in an Extensive System Using an Audio Recording Method

Avanzato R.;Avondo M.;Beritelli F.;Tumino S.
2023-01-01

Abstract

In the context of cattle farming, understanding the feeding behavior of animals is essential to ensure their welfare and maximize productivity. However, monitoring and interpret- ing the acoustic signals associated with grazing, particularly the sound events related to grass intake, pose a significant challenge. This study proposes an innovative method based on 1D convolutional neural networks to automatically classify such sound events during grazing. The approach was developed using a balanced dataset composed of 322 prehension samples and 1000 non-prehension samples, extracted from audio recordings of grazing cattle in real conditions. The results obtained show a high accuracy of 100% during the testing and validation phases of the model. However, there is concern about overfitting of the model due to the limited size of the dataset used. Consequently, future expansion of the dataset is suggested by collecting a larger and more diverse number of audio recordings to improve the generalization and robustness of the model in real cattle farming contexts.
2023
1D Convolutional Neural Networks
Audio Signal Analysis
Automatic Classification
Precision Livestock Farming
Prehension detection
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/618875
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