Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering

The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning cl...

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Bibliographic Details
Published in:Animals (Basel) 2021-02, Vol.11 (2), p.357
Main Authors: Jung, Dae-Hyun, Kim, Na Yeon, Moon, Sang Ho, Jhin, Changho, Kim, Hak-Jin, Yang, Jung-Seok, Kim, Hyoung Seok, Lee, Taek Sung, Lee, Ju Young, Park, Soo Hyun
Format: Article
Language:English
Subjects:
Acoustics
Animal husbandry
Animal welfare
Artificial intelligence
Background noise
Beef cattle
Cattle
cattle vocalization
Classification
convolutional neural network
Deep learning
Filtration
Fourier analysis
Fourier transforms
Intelligence (information)
Livestock
Meat
Meat industry
Meat processing industry
MFCC
Model accuracy
Monitoring
Neural networks
Noise
Noise monitoring
Real time
Sensors
Sound
sound classification
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Summary:The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.
ISSN:2076-2615
2076-2615
DOI:10.3390/ani11020357