Classifying Ingestive Behavior of Dairy Cows via Automatic Sound Recognition.
Sensors (Basel)
; 21(15)2021 Aug 02.
Article
en En
| MEDLINE
| ID: mdl-34372468
ABSTRACT
Determining ingestive behaviors of dairy cows is critical to evaluate their productivity and health status. The objectives of this research were to (1) develop the relationship between forage species/heights and sound characteristics of three different ingestive behaviors (bites, chews, and chew-bites); (2) comparatively evaluate three deep learning models and optimization strategies for classifying the three behaviors; and (3) examine the ability of deep learning modeling for classifying the three ingestive behaviors under various forage characteristics. The results show that the amplitude and duration of the bite, chew, and chew-bite sounds were mostly larger for tall forages (tall fescue and alfalfa) compared to their counterparts. The long short-term memory network using a filtered dataset with balanced duration and imbalanced audio files offered better performance than its counterparts. The best classification performance was over 0.93, and the best and poorest performance difference was 0.4-0.5 under different forage species and heights. In conclusion, the deep learning technique could classify the dairy cow ingestive behaviors but was unable to differentiate between them under some forage characteristics using acoustic signals. Thus, while the developed tool is useful to support precision dairy cow management, it requires further improvement.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Lactancia
/
Dieta
Tipo de estudio:
Prognostic_studies
Aspecto:
Patient_preference
Límite:
Animals
Idioma:
En
Revista:
Sensors (Basel)
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos