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The Application of Cameras in Precision Pig Farming: An Overview for Swine-Keeping Professionals.

作者信息

Arulmozhi Elanchezhian, Bhujel Anil, Moon Byeong-Eun, Kim Hyeon-Tae

机构信息

Department of Bio-Systems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Korea.

出版信息

Animals (Basel). 2021 Aug 9;11(8):2343. doi: 10.3390/ani11082343.


DOI:10.3390/ani11082343
PMID:34438800
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8388688/
Abstract

Pork is the meat with the second-largest overall consumption, and chicken, pork, and beef together account for 92% of global meat production. Therefore, it is necessary to adopt more progressive methodologies such as precision livestock farming (PLF) rather than conventional methods to improve production. In recent years, image-based studies have become an efficient solution in various fields such as navigation for unmanned vehicles, human-machine-based systems, agricultural surveying, livestock, etc. So far, several studies have been conducted to identify, track, and classify the behaviors of pigs and achieve early detection of disease, using 2D/3D cameras. This review describes the state of the art in 3D imaging systems (i.e., depth sensors and time-of-flight cameras), along with 2D cameras, for effectively identifying pig behaviors and presents automated approaches for the monitoring and investigation of pigs' feeding, drinking, lying, locomotion, aggressive, and reproductive behaviors.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6877/8388688/e7bc15bc2053/animals-11-02343-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6877/8388688/6b2fc6e13076/animals-11-02343-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6877/8388688/36685573ab4f/animals-11-02343-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6877/8388688/1d05c359291f/animals-11-02343-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6877/8388688/e7bc15bc2053/animals-11-02343-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6877/8388688/6b2fc6e13076/animals-11-02343-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6877/8388688/36685573ab4f/animals-11-02343-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6877/8388688/1d05c359291f/animals-11-02343-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6877/8388688/e7bc15bc2053/animals-11-02343-g004.jpg

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本文引用的文献

[1]
Pig-Posture Recognition Based on Computer Vision: Dataset and Exploration.

Animals (Basel). 2021-4-30

[2]
Modelling the global economic consequences of a major African swine fever outbreak in China.

Nat Food. 2020-4

[3]
Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building.

Animals (Basel). 2021-1-18

[4]
Computer Vision Applied to Detect Lethargy through Animal Motion Monitoring: A Trial on African Swine Fever in Wild Boar.

Animals (Basel). 2020-11-29

[5]
Pre-Trained Deep Convolutional Neural Network for Clostridioides Difficile Bacteria Cytotoxicity Classification Based on Fluorescence Images.

Sensors (Basel). 2020-11-24

[6]
Image Analysis and Computer Vision Applications in Animal Sciences: An Overview.

Front Vet Sci. 2020-10-21

[7]
Transforming the Adaptation Physiology of Farm Animals through Sensors.

Animals (Basel). 2020-8-26

[8]
Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs.

Sci Rep. 2020-8-12

[9]
Automated Measurement of Heart Girth for Pigs Using Two Kinect Depth Sensors.

Sensors (Basel). 2020-7-10

[10]
A Spatiotemporal Convolutional Network for Multi-Behavior Recognition of Pigs.

Sensors (Basel). 2020-4-22

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