Agricultural and Biosystems Engineering Publications

Campus Units

Agricultural and Biosystems Engineering, Animal Science, Egg Industry Center

Document Type

Article

Publication Version

Published Version

Publication Date

8-2-2021

Journal or Book Title

Sensors

Volume

21

Issue

15

First Page

5231

Research Focus Area(s)

Animal Production Systems Engineering

DOI

10.3390/s21155231

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.

Comments

This article is published as Li, Guoming, Yijie Xiong, Qian Du, Zhengxiang Shi, and Richard S. Gates. "Classifying Ingestive Behavior of Dairy Cows via Automatic Sound Recognition." Sensors 21, no. 15 (2021): 5231. DOI: 10.3390/s21155231. Posted with permission.

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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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