A context-aware method-based cattle vocal classification for livestock monitoring in smart farm

Date

2022

Authors

Sattar, Farook

Journal Title

Journal ISSN

Volume Title

Publisher

Chemistry Proceedings

Abstract

This paper focuses on livestock monitoring on a smart farm to improve animal well-being and production. The great potential for increased automation and technological innovation in agriculture could help livestock farmers to monitor the welfare of their animals for precision livestock farming. A new acoustical method exploiting contextual information is introduced for cattle vocal classification. The proposed scheme considers the raw recordings which contain cattle sounds. Then a set of contextual acoustic features is constructed as input to the MSVM classifier to track the types of cattle vocalizations. Categorized noisy cattle calls are finally classified into four types of calls (i.e., cattle food anticipating call, animal estrus call, cough sound, and normal call) with an overall classification accuracy of 84% outperforming the results obtained using conventional MFCC features. We used an open access dataset consists of 270 cattle classification records acquired using multiple sound sensors. Promising results are obtained by the proposed method for livestock monitoring enabling farm owners to determine the status of their cattle.

Description

Keywords

smart farm, cattle vocalization, classification, livestock monitoring, precision livestock farming

Citation

Farook Sattar (2022). “A context-aware method-based cattle vocal classification for livestock monitoring in smart farm.” Chemistry Proceedings, 10(1), 89. https://doi.org/10.3390/IOCAG2022-12233