A smart chicken farming platform for chicken behavior identification and feed residual estimation

Jiezhi Yang; Jun Gao; Yanyan Li; Antong Zhou; Chaochao Qu; Kuo Zhao; Linjing Wei; Le Zhang; Zirong Liu; Ning Jiang; Wenjing Tao; Kangzhe Ma; Quan Lu; Huiru Zheng

doi:10.1109/BIBM58861.2023.10385923

A smart chicken farming platform for chicken behavior identification and feed residual estimation

Jiezhi Yang, Jun Gao, Yanyan Li, Antong Zhou, Chaochao Qu, Kuo Zhao, Linjing Wei, Le Zhang, Zirong Liu, Ning Jiang, Wenjing Tao, Kangzhe Ma, Quan Lu, Huiru Zheng

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

38 Downloads (Pure)

Abstract

It is very potential to develop digital villages for promoting smart agriculture. As one of the important research fields of smart agriculture, smart chicken farms encounter management problems such as difficulties in quickly and accurately warning of sick and dead chickens and estimating feed residuals. Therefore, this study not only respectively proposed CKTrack and FRCM to detect sick and dead chickens and estimate feed residuals, but also developed a smart chicken farming platform for automagical management. Our main results include (1) the proposed CKTrack method can effectively identify sick and dead chickens under the condition of limited data volume and computing capacity; (2) the proposed FRCM method can accurately estimate the feed residuals; and (3) the smart chicken farming platform developed can provide farmers with functions such as early warning of sick and dead chickens, visualization of the chicken quantity inventory, and feed residual estimation.

Original language	English
Title of host publication	2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Editors	Xingpeng Jiang, Haiying Wang, Reda Alhajj, Xiaohua Hu, Felix Engel, Mufti Mahmud, Nadia Pisanti, Xuefeng Cui, Hong Song
Publisher	IEEE
Pages	1627-1634
Number of pages	8
ISBN (Electronic)	979-8-3503-3748-8
ISBN (Print)	979-8-3503-3749-5
DOIs	https://doi.org/10.1109/BIBM58861.2023.10385923
Publication status	Published (in print/issue) - 18 Jan 2024

Publication series

Name
Publisher	IEEE Control Society
ISSN (Print)	2156-1125
ISSN (Electronic)	2156-1133

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

computational biology
computer vision
instance segmentation
object tracking
smart chicken farming

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BIBM58861.2023.10385923

B569_3695Author Accepted Manuscript, 21 MB

Cite this

Yang, J., Gao, J., Li, Y., Zhou, A., Qu, C., Zhao, K., Wei, L., Zhang, L., Liu, Z., Jiang, N., Tao, W., Ma, K., Lu, Q., & Zheng, H. (2024). A smart chicken farming platform for chicken behavior identification and feed residual estimation. In X. Jiang, H. Wang, R. Alhajj, X. Hu, F. Engel, M. Mahmud, N. Pisanti, X. Cui, & H. Song (Eds.), 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1627-1634). IEEE. https://doi.org/10.1109/BIBM58861.2023.10385923

Yang, Jiezhi ; Gao, Jun ; Li, Yanyan et al. / A smart chicken farming platform for chicken behavior identification and feed residual estimation. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). editor / Xingpeng Jiang ; Haiying Wang ; Reda Alhajj ; Xiaohua Hu ; Felix Engel ; Mufti Mahmud ; Nadia Pisanti ; Xuefeng Cui ; Hong Song. IEEE, 2024. pp. 1627-1634

@inproceedings{de30c1dbf55748909694489c4f8a4184,

title = "A smart chicken farming platform for chicken behavior identification and feed residual estimation",

abstract = "It is very potential to develop digital villages for promoting smart agriculture. As one of the important research fields of smart agriculture, smart chicken farms encounter management problems such as difficulties in quickly and accurately warning of sick and dead chickens and estimating feed residuals. Therefore, this study not only respectively proposed CKTrack and FRCM to detect sick and dead chickens and estimate feed residuals, but also developed a smart chicken farming platform for automagical management. Our main results include (1) the proposed CKTrack method can effectively identify sick and dead chickens under the condition of limited data volume and computing capacity; (2) the proposed FRCM method can accurately estimate the feed residuals; and (3) the smart chicken farming platform developed can provide farmers with functions such as early warning of sick and dead chickens, visualization of the chicken quantity inventory, and feed residual estimation.",

keywords = "computational biology, computer vision, instance segmentation, object tracking, smart chicken farming",

author = "Jiezhi Yang and Jun Gao and Yanyan Li and Antong Zhou and Chaochao Qu and Kuo Zhao and Linjing Wei and Le Zhang and Zirong Liu and Ning Jiang and Wenjing Tao and Kangzhe Ma and Quan Lu and Huiru Zheng",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.",

year = "2024",

month = jan,

day = "18",

doi = "10.1109/BIBM58861.2023.10385923",

language = "English",

isbn = "979-8-3503-3749-5",

publisher = "IEEE",

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editor = "Xingpeng Jiang and Haiying Wang and Reda Alhajj and Xiaohua Hu and Felix Engel and Mufti Mahmud and Nadia Pisanti and Xuefeng Cui and Hong Song",

booktitle = "2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",

address = "United States",

}

Yang, J, Gao, J, Li, Y, Zhou, A, Qu, C, Zhao, K, Wei, L, Zhang, L, Liu, Z, Jiang, N, Tao, W, Ma, K, Lu, Q & Zheng, H 2024, A smart chicken farming platform for chicken behavior identification and feed residual estimation. in X Jiang, H Wang, R Alhajj, X Hu, F Engel, M Mahmud, N Pisanti, X Cui & H Song (eds), 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, pp. 1627-1634. https://doi.org/10.1109/BIBM58861.2023.10385923

A smart chicken farming platform for chicken behavior identification and feed residual estimation. / Yang, Jiezhi; Gao, Jun; Li, Yanyan et al.
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). ed. / Xingpeng Jiang; Haiying Wang; Reda Alhajj; Xiaohua Hu; Felix Engel; Mufti Mahmud; Nadia Pisanti; Xuefeng Cui; Hong Song. IEEE, 2024. p. 1627-1634.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A smart chicken farming platform for chicken behavior identification and feed residual estimation

AU - Yang, Jiezhi

AU - Gao, Jun

AU - Li, Yanyan

AU - Zhou, Antong

AU - Qu, Chaochao

AU - Zhao, Kuo

AU - Wei, Linjing

AU - Zhang, Le

AU - Liu, Zirong

AU - Jiang, Ning

AU - Tao, Wenjing

AU - Ma, Kangzhe

AU - Lu, Quan

AU - Zheng, Huiru

PY - 2024/1/18

Y1 - 2024/1/18

N2 - It is very potential to develop digital villages for promoting smart agriculture. As one of the important research fields of smart agriculture, smart chicken farms encounter management problems such as difficulties in quickly and accurately warning of sick and dead chickens and estimating feed residuals. Therefore, this study not only respectively proposed CKTrack and FRCM to detect sick and dead chickens and estimate feed residuals, but also developed a smart chicken farming platform for automagical management. Our main results include (1) the proposed CKTrack method can effectively identify sick and dead chickens under the condition of limited data volume and computing capacity; (2) the proposed FRCM method can accurately estimate the feed residuals; and (3) the smart chicken farming platform developed can provide farmers with functions such as early warning of sick and dead chickens, visualization of the chicken quantity inventory, and feed residual estimation.

AB - It is very potential to develop digital villages for promoting smart agriculture. As one of the important research fields of smart agriculture, smart chicken farms encounter management problems such as difficulties in quickly and accurately warning of sick and dead chickens and estimating feed residuals. Therefore, this study not only respectively proposed CKTrack and FRCM to detect sick and dead chickens and estimate feed residuals, but also developed a smart chicken farming platform for automagical management. Our main results include (1) the proposed CKTrack method can effectively identify sick and dead chickens under the condition of limited data volume and computing capacity; (2) the proposed FRCM method can accurately estimate the feed residuals; and (3) the smart chicken farming platform developed can provide farmers with functions such as early warning of sick and dead chickens, visualization of the chicken quantity inventory, and feed residual estimation.

KW - computational biology

KW - computer vision

KW - instance segmentation

KW - object tracking

KW - smart chicken farming

UR - http://www.scopus.com/inward/record.url?scp=85184924488&partnerID=8YFLogxK

U2 - 10.1109/BIBM58861.2023.10385923

DO - 10.1109/BIBM58861.2023.10385923

M3 - Conference contribution

SN - 979-8-3503-3749-5

SP - 1627

EP - 1634

BT - 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

A2 - Jiang, Xingpeng

A2 - Wang, Haiying

A2 - Alhajj, Reda

A2 - Hu, Xiaohua

A2 - Engel, Felix

A2 - Mahmud, Mufti

A2 - Pisanti, Nadia

A2 - Cui, Xuefeng

A2 - Song, Hong

PB - IEEE

ER -

Yang J, Gao J, Li Y, Zhou A, Qu C, Zhao K et al. A smart chicken farming platform for chicken behavior identification and feed residual estimation. In Jiang X, Wang H, Alhajj R, Hu X, Engel F, Mahmud M, Pisanti N, Cui X, Song H, editors, 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE. 2024. p. 1627-1634 doi: 10.1109/BIBM58861.2023.10385923

A smart chicken farming platform for chicken behavior identification and feed residual estimation

Abstract

Publication series

Bibliographical note

Keywords

UN SDGs

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