scholarly journals Automation of video-based location tracking tool for dairy cows in their housing stalls using deep learning

2021 ◽  
Vol 1 ◽  
pp. 100015
Author(s):  
A. Zambelis ◽  
M. Saadati ◽  
G.M. Dallago ◽  
P. Stecko ◽  
V. Boyer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dairy Cows ◽  
Location Tracking

scholarly journals Predicting bovine tuberculosis status of dairy cows from mid-infrared spectral data of milk using deep learning

2020 ◽  
Vol 103 (10) ◽  
pp. 9355-9367
Author(s):  
S.J. Denholm ◽  
W. Brand ◽  
A.P. Mitchell ◽  
A.T. Wells ◽  
T. Krzyzelewski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dairy Cows ◽  
Spectral Data ◽  
Bovine Tuberculosis ◽  
Mid Infrared ◽  
Infrared Spectral

scholarly journals Classifying Ingestive Behavior of Dairy Cows via Automatic Sound Recognition

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5231
Author(s):  
Guoming Li ◽  
Yijie Xiong ◽  
Qian Du ◽  
Zhengxiang Shi ◽  
Richard S. Gates
Keyword(s):  
Deep Learning ◽  
Dairy Cows ◽  
Dairy Cow ◽  
Short Term Memory ◽  
Classification Performance ◽  
Ingestive Behavior ◽  
Sound Recognition ◽  
Forage Species ◽  
Audio Files ◽  
Ingestive Behaviors

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.

<i>An Imaging System Based on Deep Learning for Monitoring the Feeding Behavior of Dairy Cows</i>

2019 ◽  
Author(s):  
Cheng Yu Kuan ◽  
Yu Chi Tsai ◽  
Jih Tay Hsu ◽  
Shih Torng Ding ◽  
Ta Te Lin
Keyword(s):  
Deep Learning ◽  
Feeding Behavior ◽  
Dairy Cows ◽  
Imaging System

Predicting the milk yield curve of dairy cows in the subsequent lactation period using deep learning

2021 ◽  
Vol 180 ◽  
pp. 105904
Author(s):  
Arno Liseune ◽  
Matthieu Salamone ◽  
Dirk Van den Poel ◽  
Bonifacius van Ranst ◽  
Miel Hostens
Keyword(s):  
Deep Learning ◽  
Dairy Cows ◽  
Milk Yield ◽  
Yield Curve ◽  
Lactation Period

A computer vision approach based on deep learning for the detection of dairy cows in free stall barn

2021 ◽  
Vol 182 ◽  
pp. 106030
Author(s):  
Patrizia Tassinari ◽  
Marco Bovo ◽  
Stefano Benni ◽  
Simone Franzoni ◽  
Matteo Poggi ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Dairy Cows

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning
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