research-article

Cow Individual Identification Based on Convolutional Neural Network

Authors:

Zhangyong Li,

Chao Ge,

Siwan Shen,

Xinwei LiAuthors Info & Claims

ACAI '18: Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence

Article No.: 45, Pages 1 - 5

https://doi.org/10.1145/3302425.3302460

Published: 21 December 2018 Publication History

Get Access

Abstract

With the advent of the artificial intelligence era, the intelligent monitoring video analysis technology is deeply applied to the management decision of livestock and poultry farming, and the individual identification of dairy cows can improve the efficiency of video analysis. In this paper, an individual identification method based on convolutional neural network is proposed for the automation and precision of dairy cows. Using the video capture device to obtain the 360° torso information of the cow, the cow's video is obtained by frame acquisition to obtain the cow's torso image information, and the residual learning deconvolution neural network is used to denoise the 21600 cow images. Randomly selects pictures to define the training set and testing set as the ratio of 9:1 and selects the InceptionV3 network to train the main network of the individual identification of the cows. Through the cross-validation of the training set and the test set, the top_1 recognition accuracy rate of the single image can reach 87%, and the top_3 recognition accuracy rate is 93%.Through the improvement of InceptionV3 neural network, the top_1 recognition accuracy of multiple images can reach 92%, and the recognition accuracy of top_3 is 95%.

References

[1]

Zhu Weixing, Pu Xuefeng, Li Xincheng, et al. Automatic identification system of pigs with suspected case based on behavior monitoring{J}. Transaction of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(1): 188--192.(in Chinese with English abstract)

Google Scholar

[2]

Chapinal N, Tucker C B. Validation of an automated method to count steps while cows stand on a weighing platform and its application as a measure to detect lameness{J}. Journal of Dairy Science, 2012, 95(11): 6523--6528.

Crossref

Google Scholar

[3]

Pluk A, Bahr C, Poursaberi A, et al. Automatic measurement of touch and release angles of the fetlock joint for lameness detection in dairy cattle using vision techniques{J}. Journal of Dairy Science, 2012, 95(4):1738--1748.

Crossref

Google Scholar

[4]

Viazzi S, Bahr C, Schlageter-Tello A, et al. Analysis of individual classification of lameness using automatic measurement of back posture in dairy cattle{J}. Journal of Dairy Science, 2013, 96(1):257--266. {1} Mitchell. Machine Learning{M}. McGraw-Hill, 2003.

Crossref

Google Scholar

[5]

Zhao Kaixuan, He Dongjian. Recognition of individual dairy cattle based on convolutional neural networks{J}. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31(5): 181--187. (in Chinese with English abstract)

Google Scholar

[6]

Lecun Y, Bengio Y, Hinton G. Deep learning{J}. Nature, 2015, 521(7553):436

Crossref

Google Scholar

[7]

Mitchell. Machine Learning{M}. McGraw-Hill, 2003.

Google Scholar

[8]

Zhang K, Zuo W, Chen Y, et al. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising.{J}. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2017, PP(99):1--1.

Digital Library

Google Scholar

[9]

Dabov K, Foi A, Katkovnik V, et al. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering{J}. IEEE Transactions on Image Processing, 2007, 16(8):2080

Digital Library

Google Scholar

[10]

Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the Inception Architecture for Computer Vision{C}// Computer Vision and Pattern Recognition. IEEE, 2016:2818--2826.

Google Scholar

[11]

Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions{C}// Computer Vision and Pattern Recognition. IEEE, 2015:1--9.v

Google Scholar

[12]

Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift{C}// International Conference on International Conference on Machine Learning. JMLR.org, 2015:448--456.

Digital Library

Google Scholar

Cited By

View all

Djibrine OAhmat DBoukar M(2024)Deep Learning-based Approaches for Preventing and Predicting Wild Animals Disappearance: A Review2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)10.1109/ACDSA59508.2024.10467213(1-6)Online publication date: 1-Feb-2024
https://doi.org/10.1109/ACDSA59508.2024.10467213
Li LZhang TCuo DZhao QZhou LJiancuo S(2023)Automatic identification of individual yaks in in-the-wild images using part-based convolutional networks with self-supervised learningExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119431216:COnline publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119431
Weerasinghe GKarunanayaka KKumarasinghe PPerera DTrenado CZoysa KKeppitiyagama C(2022)Identification of Previously Unseen Asian Elephants using Visual Data and Semi-Supervised Learning2022 22nd International Conference on Advances in ICT for Emerging Regions (ICTer)10.1109/ICTer58063.2022.10024068(019-024)Online publication date: 30-Nov-2022
https://doi.org/10.1109/ICTer58063.2022.10024068
Show More Cited By

Index Terms

Cow Individual Identification Based on Convolutional Neural Network
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics

Recommendations

The Classification of Hypertensive Retinopathy using Convolutional Neural Network

Changes in the retina of the eyes may occur due to high blood pressure, hypertensive retinopathy (HR) is a type of eye disease in which there is a change of the blood vessels of the eyes in the eye retina caused by arterial hypertension. HR signs occur ...
A deep convolutional neural network model to classify heartbeats

The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of ...
Electrocardiogram classification of lead convolutional neural network based on fuzzy algorithm
Fuzzy Systems for Medical Image Analysis

With the development of society, health has attracted more and more attention. Heart disease is a common and frequently occurring disease, and it is fatal. Rapid and timely diagnosis and treatment of heart disease is very important. Electrocardiogram (ECG)...

Comments

Information & Contributors

Information

Published In

ACAI '18: Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence

December 2018

460 pages

ISBN:9781450366250

DOI:10.1145/3302425

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

The Hong Kong Polytechnic: The Hong Kong Polytechnic University
City University of Hong Kong: City University of Hong Kong

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 December 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ACAI 2018

ACAI 2018: 2018 International Conference on Algorithms, Computing and Artificial Intelligence

December 21 - 23, 2018

Sanya, China

Acceptance Rates

ACAI '18 Paper Acceptance Rate 76 of 192 submissions, 40%;

Overall Acceptance Rate 173 of 395 submissions, 44%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
396
Total Downloads

Downloads (Last 12 months)25
Downloads (Last 6 weeks)2

Reflects downloads up to 26 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Djibrine OAhmat DBoukar M(2024)Deep Learning-based Approaches for Preventing and Predicting Wild Animals Disappearance: A Review2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)10.1109/ACDSA59508.2024.10467213(1-6)Online publication date: 1-Feb-2024
https://doi.org/10.1109/ACDSA59508.2024.10467213
Li LZhang TCuo DZhao QZhou LJiancuo S(2023)Automatic identification of individual yaks in in-the-wild images using part-based convolutional networks with self-supervised learningExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119431216:COnline publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119431
Weerasinghe GKarunanayaka KKumarasinghe PPerera DTrenado CZoysa KKeppitiyagama C(2022)Identification of Previously Unseen Asian Elephants using Visual Data and Semi-Supervised Learning2022 22nd International Conference on Advances in ICT for Emerging Regions (ICTer)10.1109/ICTer58063.2022.10024068(019-024)Online publication date: 30-Nov-2022
https://doi.org/10.1109/ICTer58063.2022.10024068
da Rosa Righi RGoldschmidt GKunst RDeon CAndré da Costa C(2022)Towards combining data prediction and internet of things to manage milk production on dairy cowsComputers and Electronics in Agriculture10.1016/j.compag.2019.105156169:COnline publication date: 21-Apr-2022
https://dl.acm.org/doi/10.1016/j.compag.2019.105156
Li GHuang YChen ZChesser GPurswell JLinhoss JZhao Y(2021)Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A ReviewSensors10.3390/s2104149221:4(1492)Online publication date: 21-Feb-2021
https://doi.org/10.3390/s21041492
Li SFu LSun YMu YChen LLi JGong H(2021)Individual dairy cow identification based on lightweight convolutional neural networkPLOS ONE10.1371/journal.pone.026051016:11(e0260510)Online publication date: 29-Nov-2021
https://doi.org/10.1371/journal.pone.0260510
Kulits PWall JBedetti AHenley MBeery S(2021)ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-IdentificationProceedings of the 4th ACM SIGCAS Conference on Computing and Sustainable Societies10.1145/3460112.3471947(88-98)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1145/3460112.3471947
Zhang TZhao QDa CZhou LLi LJiancuo S(2021)YakReID-103: A Benchmark for Yak Re-Identification2021 IEEE International Joint Conference on Biometrics (IJCB)10.1109/IJCB52358.2021.9484341(1-8)Online publication date: 4-Aug-2021
https://dl.acm.org/doi/10.1109/IJCB52358.2021.9484341
Bergler CGebhard ATowers JButyrev LSutton GShaw TMaier ANöth E(2021)FIN-PRINT a fully-automated multi-stage deep-learning-based framework for the individual recognition of killer whalesScientific Reports10.1038/s41598-021-02506-611:1Online publication date: 6-Dec-2021
https://doi.org/10.1038/s41598-021-02506-6
Monteiro JBarata J(2021)Artificial Intelligence in Extended Agri-Food Supply Chain: A Short Review Based on Bibliometric AnalysisProcedia Computer Science10.1016/j.procs.2021.09.074192(3020-3029)Online publication date: 2021
https://doi.org/10.1016/j.procs.2021.09.074
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

The Classification of Hypertensive Retinopathy using Convolutional Neural Network

A deep convolutional neural network model to classify heartbeats

Electrocardiogram classification of lead convolutional neural network based on fuzzy algorithm

Comments

Published In

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

The Classification of Hypertensive Retinopathy using Convolutional Neural Network

A deep convolutional neural network model to classify heartbeats

Electrocardiogram classification of lead convolutional neural network based on fuzzy algorithm

Comments

Information

Published In

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Get Access

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations