All Issue

2026 Vol.8, Issue 1 Preview Page

Review Article

Trends in video-based monitoring technologies for broiler houses: Camera systems and computer vision techniques
Yeong-Gil Kim1
,
Min-Young Hwang2
,
Jin-Young Lee2
,
Jeong-Chan Lee2
,
Jae-Min Lee2
,
Ryu-Gap Lim2*†
,
Cheol-Woo Han1*
1Department of Bio-systems & Bioengineering, Korea Polytechnic University, Gimje 54323, Republic of Korea
2Department of Fusion Biosystems Mechanical Engineering, Sunchon National University, Suncheon 57922, Republic of Korea
*Corresponding Author
†These authors equally contributed to this study as corresponding author.
31 March 2026. pp. 66-82
PDF XML Citation
Abstract

In large-scale, intensive broiler production systems, continuous and quantitative assessment of individual condition and flock behavior through visual observation alone is inherently limited because of high stocking density, variations in lighting and environmental conditions, and the visual similarity among birds. Accordingly, considerable research has focused on the use of cameras and computer vision technologies for non-contact, automated monitoring of broiler location, distribution, activity, behavior, and abnormal signs. This review examines research trends in video-based monitoring technologies for commercial broiler houses and systematically summarizes the characteristics of broiler-house video data, camera system configurations, and the application of major computer vision techniques. Specifically, it reviews the visual and environmental characteristics of broiler-house video data, including high-density flocking, bird overlap, occlusion caused by feeders and drinkers, the complexity of litter backgrounds, lighting variation, and age-related changes in appearance and behavior patterns. In addition, the potential applications of xxx overhead cameras, side-view cameras, pan-tilt-zoom (PTZ) cameras, multi-camera systems, thermal imaging, and depth sensors are compared, and the applications and limitations of key computer vision approaches, including object detection, object tracking, segmentation, behavior analysis, and anomaly detection, are analyzed. A synthesis of previous studies indicates that video-based monitoring has significant potential for bird counting, flock distribution analysis, activity assessment, welfare monitoring, and early detection of health abnormalities. However, under practical commercial conditions, major challenges remain, including bird overlap, re-identification errors, limited dataset generalizability, constraints in real-time processing, and insufficient long-term operational stability. Future research should therefore focus on the development of multi-farm and multi-view datasets, the integrated design of camera systems and analytical algorithms, real-time processing architectures, and interpretable indicators that support managerial decision-making. By systematically reviewing the research trends and limitations of video-based monitoring technologies in broiler houses, this review provides a foundation for the design of integrated monitoring systems with high field applicability.

Keywords
Broiler chicken
Broiler house
Computer vision
Precision livestock farming
References
1

Campbell, M., Miller, P., Díaz-Chito, K., Hong, X., McLaughlin, N., Parvinzamir, F., Del Rincón, J. M., O’Connell, N. 2024. A computer vision approach to monitor activity in commercial broiler chickens using trajectory-based clustering analysis. Computers and Electronics in Agriculture 217: 108591. https://doi.org/10.1016/j.compag.2023.108591

10.1016/j.compag.2023.108591
2

Dawkins, M.S., Lee, H.-J., Waitt, C.D., Roberts, S.J. 2009. Optical flow patterns in broiler chicken flocks as automated measures of behaviour and gait. Applied Animal Behaviour Science 119(3-4): 203-209. https://doi.org/10.1016/j.applanim.2009.04.009

10.1016/j.applanim.2009.04.009
3

Dawkins, M.S., Wang, L., Ellwood, S.A., Roberts, S.J., Gebhardt-Henrich, S.G. 2021. Optical flow, behaviour and broiler chicken welfare in the UK and Switzerland. Applied Animal Behaviour Science 243: 105475. https://doi.org/10.1016/j.applanim.2020.105180

10.1016/j.applanim.2020.105180
4

Deep, A., Schwean-Lardner, K., Crowe, T.G., Fancher, B.I., Classen, H.L. 2012. Effect of light intensity on broiler behaviour and diurnal rhythms. Applied Animal Behaviour Science 136(1): 50-56. https://doi.org/10.1016/j.applanim.2011.11.002

10.1016/j.applanim.2011.11.002
5

Guo, Y., Aggrey, S.E., Oladeinde, A., Johnson, J., Zock, G., Chai, L. 2021. A machine vision-based method optimized for restoring broiler chicken images occluded by feeding and drinking equipment. Animals 11(1): 123. https://doi.org/10.3390/ani11010123

10.3390/ani1101012333429972PMC7826920
6

Guo, Y., Aggrey, S.E., Wang, P., Oladeinde, A., Chai, L. 2022. Monitoring behaviors of broiler chickens at different ages with deep learning. Animals 12(23): 3390. https://doi.org/10.3390/ani12233390

10.3390/ani1223339036496910PMC9736866
7

Guo, Y., Aggrey, S.E., Yang, X., Oladeinde, A., Qiao, Y., Chai, L. 2023. Detecting broiler chickens on litter floor with the YOLOv5-CBAM deep learning model. Artificial Intelligence in Agriculture 9: 36-45. https://doi.org/10.1016/j.aiia.2023.08.002

10.1016/j.aiia.2023.08.002
8

Guo, Y., Chai, L., Aggrey, S.E., Oladeinde, A., Johnson, J., Zock, G. 2020. A machine vision-based method for monitoring broiler chicken floor distribution. Sensors 20(11): 3179. https://doi.org/10.3390/s20113179

10.3390/s2011317932503296PMC7309057
9

Jaihuni, M., Gan, H., Tabler, T., Prado, M., Qi, H., Zhao, Y. 2023. Broiler mobility assessment via a semi-supervised deep learning model and Neo-Deep Sort algorithm. Animals 13(17): 2719. https://doi.org/10.3390/ani13172719

10.3390/ani1317271937684983PMC10487264
10

Kristensen, H.H., Perry, G.C., Prescott, N.B., Ladewig, J., Ersbøll, A.K., Wathes, C.M. 2007. The behaviour of broiler chickens in different light sources and illuminances. Applied Animal Behaviour Science 103(1-2): 75-89. https://doi.org/10.1016/j.applanim.2006.04.017

10.1016/j.applanim.2006.04.017
11

Li, G., Kota, S.A. R., Liu, T., Oso, O.M., Bodempudi, V.U. C., Saeidifar, M., Asali, E., Mandiga, A., Lu, J., Yuan, G., Banakar, A. 2025. Enhancing consistency and efficiency of tracking multiple individual broiler chickens in group settings. Smart Agricultural Technology 12: 101408. https://doi.org/10.1016/j.atech.2025.101408

10.1016/j.atech.2025.101408
12

Li, N., Ren, Z., Li, D., Zeng, L. 2020. Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: Towards the goal of precision livestock farming. Animal 14(3): 617-625. https://doi.org/10.1017/S1751731119002155

10.1017/S1751731119002155
13

Li, X., Cai, M., Tan, X., Yin, C., Chen, W., Liu, Z., Wen, J., Han, Y. 2024. An efficient transformer network for detecting multi-scale chicken in complex free-range farming environments via improved RT-DETR. Computers and Electronics in Agriculture 224: 109160. https://doi.org/10.1016/j.compag.2024.109160

10.1016/j.compag.2024.109160
14

Norton, T., Chen, C., Larsen, M.L.V., Berckmans, D. 2019. Review: Precision livestock farming: Building ‘digital representations’ to bring the animals closer to the farmer. Animal 13(12): 3009-3017. https://doi.org/10.1017/S175173111900199X

10.1017/S175173111900199X
15

Okinda, C., Nyalala, I., Korohou, T., Okinda, C., Wang, J., Achieng, T., Wamalwa, P., Mang, T., Shen, M. 2020. A review on computer vision systems in monitoring of poultry: A welfare perspective. Artificial Intelligence in Agriculture 4: 184-208. https://doi.org/10.1016/j.aiia.2020.09.002

10.1016/j.aiia.2020.09.002
16

Qi, H., Chen, Z., Liang, G., Chen, R., Jiang, J., Luo, X. 2025. Broiler behavior detection and tracking method based on lightweight transformer. Applied Sciences 15(6): 3333. https://doi.org/10.3390/app15063333

10.3390/app15063333
17

Rowe, E., Dawkins, M.S., Gebhardt-Henrich, S.G. 2019. A systematic review of precision livestock farming in the poultry sector: Is technology focussed on improving bird welfare? Animals 9(9): 614. https://doi.org/10.3390/ani9090614

10.3390/ani909061431461984PMC6770384
18

Statistics Korea. 2025. 2025 livestock trends survey report for the first quarter (as of March 1, 2025).

10.5089/9798229020664.019
19

Shah, S.B., Earnest, K., Oviedo-Rondón, E.O., Kolar, P., Singletary, I. 2022. Simultaneous reduction of thermal stratification and ammonia concentrations in poultry house during brooding and in cool weather. Applied Engineering in Agriculture 38(2): 375-386. https://doi.org/10.13031/aea.14862

10.13031/aea.14862
20

Korean Statistical Information Service (KOSIS). 2025. Meat production [Table DT_1ZGA5C].

21

Tóth, G., Hornyák, Á., Várkonyi, E.P., Kövér, G.Y., Jánosi, A., Balaskó, Á., Szűcs, I., Huzsvai, L. 2025. Edge or cloud architecture: The applicability of new data processing methods in large-scale poultry farming. Technologies 13(1): 17. https://doi.org/10.3390/technologies13010017

10.3390/technologies13010017
22

Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., Chen, J. 2024. DETRs beat YOLOs on real-time object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition: 16965-16974. https://doi.org/10.1109/CVPR52733.2024.01605

10.1109/CVPR52733.2024.01605
Information
  • Publisher :Korean Society of Precision Agriculture
  • Publisher(Ko) :한국정밀농업학회
  • Journal Title :Precision Agriculture Science and Technology
  • Journal Title(Ko) :정밀농업과학기술
  • Volume : 8
  • No :1
  • Pages :66-82
  • Received Date : 2026-03-13
  • Revised Date : 2026-03-26
  • Accepted Date : 2026-03-26
  • DOI :https://doi.org/10.22765/pastj.20260006
Journal Informaiton Precision Agriculture Science and Technology Precision Agriculture Science and Technology
Online Submission & Review System
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • KCI 문헌 유사도 검사
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close