Skip to main content

Advertisement

SpringerLink
Log in

Machine learning and deep learning techniques for poultry tasks management: a review

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In recent years the poultry production industry has adopted automation with the help of different kinds of technological advancements like verities of monitoring and sensing tools, IoT devices, sensors, monitoring devices, and more. These advanced techniques will offer numerous advantages in poultry product production. Only with human resources based large-scale poultry production is not an easy task because the public health threads from ingesting foods with high antibiotic remains will residues a problem. Sometimes, zoonotic diseases and foodborne diseases present a crucial task to poultry producers. These repeated tasks concern massive and hazardous work accomplished within a suffering work domain, leading to high human health safety, labor cost, and sustainable risk of cross-infection through manufacturing facilities. In recent years, Artificial Intelligence technology has played a vital role in all sectors including poultry production has utilized advanced Machine Learning and Deep Learning technologies which is a subfield of AI, this technology has an acceptable potential to manage numerous challenges in the establishment of information-based farming and various task-handling systems in the poultry production sector. This article comprehensively reviews some recently established machine learning and deep learning algorithms for poultry management tasks like chicken activity monitoring, farm weather monitoring and control, weight prediction, earlier identification of diseased chickens, and more. It is divine that this proposed review work will compose a piece of helpful information for all who are interested in enhancing recognition of the future scope of utilizing the ML and DL techniques in the poultry production sectors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data Availability

This paper has no dataset.

References

  1. Arpita M, Deblina M, Maneesha M, Snigdha S (2021) Poultry Farming: Prospects and Impediments in India. Saudi J. Humanities Soc Sci, 6(6): 193–198. https://saudijournals.com/media/articles/SJHSS_66_193-198.pdf. Accessed 23/111/2021

  2. Brijesh M, Saad BE, Semida S (2017) Greenhouse gas mitigation using poultry litter management techniques in Bangladesh. Energy 127:155–166. https://doi.org/10.1016/j.energy.2017.03.103

    Article  Google Scholar 

  3. Olivier D, Saïd M, Pierre M, Frédéric L (2022) Cloud and distributed architectures for data management in agriculture 4.0: Review and future trends. Comput Inform Sci 34:7494–7514. https://doi.org/10.1016/j.jksuci.2021.09.015

    Article  Google Scholar 

  4. Khan R, Ben Ayed R, Hanana M (2021) Artificial Intelligence to Improve the Food and Agriculture Sector. Journal of Food Quality, Hindawi. 2021: https://doi.org/10.1155/2021/5584754

  5. Sarker IH (2021) Machine Learning: Algorithms, Real-World Applications and Research Directions. SN COMPUT. SCI. 2, 160). https://doi.org/10.1007/s42979-021-00592-x

  6. Neha S, Reecha S, Neeru J (2021) Machine Learning and Deep Learning Applications-A Vision. Global Trans Proc 2:24–28. https://doi.org/10.1016/j.gltp.2021.01.004

    Article  Google Scholar 

  7. Sébastien V, David M, Marc C, Emily SD, Gérard S, Thomas C, Sébastien V (2018) A Deep learning method for accurate and fast identification of coral reef fishes in underwater images. Eco Inform 48:238–244. https://doi.org/10.1016/j.ecoinf.2018.09.007

    Article  Google Scholar 

  8. Thavamani S, Vijayakumar J, Sruthi K (2021) GLCM and K-Means based Chicken Gender Classification. IEEE. Conference Proceedings. Smart Technologies, Communication and Robotics (STCR) 1–5. doi: https://doi.org/10.1109/STCR51658.2021.9588864

  9. Chowdhury K, Chaudhuri D, Pal AK (2021) An entropy-based initialization method of K-means clustering on the optimal number of clusters. Neural Comput Applic 33:6965–6982. https://doi.org/10.1007/s00521-020-05471-9

    Article  Google Scholar 

  10. Olivier D, Saïd M, Sidi AM, Pierre M, Jérôme B, Frédéric L (2020) Edge Computing and Artificial Intelligence for Real-time Poultry Monitoring. Procedia Computer Science 175:534–541. https://doi.org/10.1016/j.procs.2020.07.076

    Article  Google Scholar 

  11. Berckmans D (2014Apr) Precision livestock farming technologies for welfare management in intensive livestock systems. Rev Sci Tech 33(1):189–196. https://doi.org/10.20506/rst.33.1.2273

    Article  Google Scholar 

  12. Guo Y, Chai L, Aggrey SE, Oladeinde A, Johnson J, Zock G, (2020) A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution. J Sensors. 20: 1424–8220. https://www.mdpi.com/1424-8220/20/11/3179

  13. Chakchai SI, Sarayut P, Kanokmon R (2014) A hybrid mobile environmental and population density management system for smart poultry farms. Comput Electron Agric 109:287–301. https://doi.org/10.1016/j.compag.2014.10.004

    Article  Google Scholar 

  14. Seo D, Cho S, Manjula P, Choi N, Kim Y-K, Koh YJ, Lee SH, Kim H-Y, Lee JH (2021) Identification of Target Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs. Animals 11(1):241. https://doi.org/10.3390/ani11010241

    Article  Google Scholar 

  15. Cheng F, Junduan H, Kaixuan C, Xiaolin Z, Tiemin Z (2020) Comparative study on poultry target tracking algorithms based on a deep regression network. Biosys Eng 190:176–183. https://doi.org/10.1016/j.biosystemseng.2019.12.002

    Article  Google Scholar 

  16. Xiong X, Mingzhou L, Weizhong Y, Guanghui D, Qingyan Y, Mingxia S, Tomas N, Daniel B (2019) An Automatic Head Surface Temperature Extraction Method for Top-View Thermal Image with Individual Broiler. Sensors 19(23):5286. https://doi.org/10.3390/s19235286

    Article  Google Scholar 

  17. Sylvio B, Ana Paula Ayub da CB,Rafael GM, Douglas FB, (2018) Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification. J Spectrosc, Hindawi. 2018: 2314-4920https://doi.org/10.1155/2018/8949741

  18. Pereira DF, Miyamoto BCB, Maia GDN, Tatiana Sales G, Magalhães MM, Richard SG (2013) Machine vision to identify broiler breeder behavior. Comput Electron Agric 99:194–199. https://doi.org/10.1016/j.compag.2013.09.012

    Article  Google Scholar 

  19. Juan W, Nan W, Lihua L (2020) Real-time behavior detection and judgment of egg breeders based on YOLO v3. Neural Comput Applic 32:5471–5481. https://doi.org/10.1007/s00521-019-04645-4

    Article  Google Scholar 

  20. Guoming L, Boyu J, Baoming L, Zhengxiang S, Yang Z, Yangqin D, John B (2020) Assessment of layer pullet drinking behaviors under selectable light colors using convolutional neural network. Comput Electron Agric 172:0168–1699. https://doi.org/10.1016/j.compag.2020.105333

    Article  Google Scholar 

  21. Guoming L, Xue H, Zhiqian C, Gary DC, Yang Z (2021) Development and evaluation of a method to detect broilers continuously walking around feeder as an indication of restricted feeding behaviors. Comput Electron Agric 181:0168–1699. https://doi.org/10.1016/j.compag.2020.105982

    Article  Google Scholar 

  22. Jihao Y, Edmond L, Mohammad A, Nicole MZ, Martin JZ (2021) Using an artificial neural network to predict the probability of oviposition events of precision-fed broiler breeder hens. Poult Sci 100:0032–5791. https://doi.org/10.1016/j.psj.2021.101187

    Article  Google Scholar 

  23. Chen Y, Wang SC (2018) Poultry carcass visceral contour recognition method using image processing. J Appl Poultry Res 27:316–324. https://doi.org/10.3382/japr/pfx073

    Article  Google Scholar 

  24. Irenilza de AN, Nilsa Duarte da SL, Rodrigo FG, Luiz Antonio de L, Henry U, Jair MA (2021) Lameness prediction in broiler chicken using a machine learning technique. Inform Proc Agricult. 8: 409-418 https://doi.org/10.1016/j.inpa.2020.10.003

  25. Alison ZL, Tadayuki YJ, Wilian SL, Giovanni R (2014) Predicting Rectal Temperature of Broiler Chickens with Artificial Neural Network. Int J Eng Technol IJET-IJENS. 14:5 http://repositorio.ufla.br/jspui/handle/1/37484

  26. Yang X, Zhao Y, Street GM, Huang Y, Filip To SD, Purswell JL (2021) Classification of broiler behaviours using triaxial accelerometer and machine learning. Animal 15:1751–7311. https://doi.org/10.1016/j.animal.2021.100269

    Article  Google Scholar 

  27. Guoming L, Yang Z, Gray DC, Jr., John WL, Joseph LP (2019) Image processing for analyzing broiler feeding and drinking behaviors. American Society of Agricultural and Biological Engineers. 1: 1900165. https://doi.org/10.13031/aim.201900165

  28. Nolasco Perez IM, Badaró AT, Barbon S, Barbon APA, Pollonio MAR, Barbin DF (2018) Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning. Appl Spectrosc 72(12):1774–1780. https://doi.org/10.1177/0003702818788878

    Article  Google Scholar 

  29. Junduan H, Tiemin Z, Kaixuan C, Cheng F (2021) An intelligent method for detecting poultry eating behaviour based on vocalization signals. Comput Electron Agric 180:0168–1699. https://doi.org/10.1016/j.compag.2020.105884

    Article  Google Scholar 

  30. Jihao Y, Edmond L, Mohammad A, Nicole MZ, Martin JZ (2021) A supervised machine learning method to detect anomalous real-time broiler breeder body weight data recorded by a precision feeding system. Comput Electron Agric 185:0168–1699. https://doi.org/10.1016/j.compag.2021.106171

    Article  Google Scholar 

  31. Somaye A, Saman AM, Somayeh S (2017) Application of computer vision and support vector regression for weight prediction of live broiler chicken. Eng Agricult, Environ Food 10:266–271. https://doi.org/10.1016/j.eaef.2017.04.003

    Article  Google Scholar 

  32. De Wet L, Vranken E, Chedad A, Aerts JM, Ceunen J, Berckmans D (2003) Computer-assisted image analysis to quantify daily growth rates of broiler chickens. Br Poult Sci 44(4):524–532. https://doi.org/10.1080/00071660310001616192

    Article  Google Scholar 

  33. Al-Nedawi AM (2019). Some of the body measurements and their role as predictors of final weight using all possible regressions procedure in commercial broilers. Adv Anim Vet Sci 7(3): 181–186. https://doi.org/10.17582/journal.aavs/2019/7.3.181.186

  34. Lech A, Marta C, Tomasz F, Dorota P, Marcin W, Tomasz B (2018) The use of 3D scanning to determine the weight of the chicken breast. Comput Electron Agric 155:394–399. https://doi.org/10.1016/j.compag.2018.10.039

    Article  Google Scholar 

  35. Akanmode ER, Oye ND, Celestine HR (2018) Prediction of Poultry Yield Using Data Mining Techniques. Int J Innovation Eng Sci Res 2: 16–32. https://www.ijiesr.com/liebrary/e15/9114259952.pdf

  36. Chun-YW, Ying-JC, Chen-FC (2021) Industry 3.5 to empower smart production for poultry farming and an empirical study for broiler live weight prediction. Comput Indust Eng 151: 0360-8352https://doi.org/10.1016/j.cie.2020.106931

  37. Yike C, Yibin Y, Xiuqin R (2006) Egg weight detection on machine vision system. Proceedings Volume 6381, Optics for Natural Resources, Agriculture, and Foods; 638114. https://doi.org/10.1117/12.686479

  38. Zaninelli M, Veronica R, Fabio L, Malcolm M, Valentino B, Donata C, Vittorio D, Giovanni S (2018) Development of a Machine Vision Method for the Monitoring of Laying Hens and Detection of Multiple Nest Occupations. Sensors 18(1):132. https://doi.org/10.3390/s18010132

    Article  Google Scholar 

  39. Joko S, Endah A, Siswantoro MZFN (2022) A rapid and accurate computer vision system for measuring the volume of axi-symmetric natural products based on cubic spline interpolation. J Food Eng 333:0260–8774. https://doi.org/10.1016/j.jfoodeng.2022.111139

    Article  Google Scholar 

  40. Taiwo GO, Olusola OA, Adebola OO (2020) Fuzzy prediction and pattern analysis of poultry egg production. Comput Electron Agric 171:0168–1699. https://doi.org/10.1016/j.compag.2020.105301

    Article  Google Scholar 

  41. Alon AS, Mindoro J, Casuat CD, Marasigan RI (2019) An image processing approach of multiple eggs’ quality inspection. Int J Adv Trends Comput Sci Eng 8(6):2794–2799. https://www.warse.org/IJATCSE/static/pdf/file/ijatcse18862019.pdf. Accessed 18/11/2021

  42. Pattison M, McMullin P, Bradbury JM, Alexander D (Eds.) (2007) Poultry diseases. Elsevier Health Sciences. https://shop.elsevier.com/books/poultry-diseases/pattison/978-0-7020-2862-5. Accessed 18/11/2021

  43. Jintao W, Shen M, Longshen L, Yi X, Cedric O (2019) Recognition and Classification of Broiler Droppings Based on Deep Convolutional Neural Network. Hindawi J Sensor 2019:3823515. https://doi.org/10.1155/2019/3823515

    Article  Google Scholar 

  44. Zhang H, Chen C (2020) Design of Sick Chicken Automatic Detection System Based on Improved Residual Network. IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). Chongqing, China 2020:2480–2485. https://doi.org/10.1109/ITNEC48623.2020.9084666

    Article  Google Scholar 

  45. Cedric O, Mingzhou L, Longshen L, Innocent N, Caroline M, Jintao W, Hailin Z, Mingxia S (2019) A machine vision system for early detection and prediction of sick birds: A broiler chicken model. Biosys Eng 188:229–242. https://doi.org/10.1016/j.biosystemseng.2019.09.015

    Article  Google Scholar 

  46. Chase EG, Michael JR, Abhinav M (2019) Comparison between random forest and gradient boosting machine methods for predicting Listeria spp. prevalence in the environment of pastured poultry farms. Food Res Int 122:47–55. https://doi.org/10.1016/j.foodres.2019.03.062

    Article  Google Scholar 

  47. Varsha Sahni, Sandeep Srivastava, Rijwan Khan, "Modelling Techniques to Improve the Quality of Food Using Artificial Intelligence", Journal of Food Quality, vol. 2021, Article ID 2140010, 10 pages, 2021. https://doi.org/10.1155/2021/2140010

  48. Singh G, Sethi GK, Singh S (2021) Survey on Machine Learning and Deep Learning Techniques for Agriculture Land. SN Comput Sci 2:487. https://doi.org/10.1007/s42979-021-00929-6

    Article  Google Scholar 

  49. Debauche O, Elmoulat M, Mahmoudi S, Bindelle J, Lebeau F (2021). Farm Animals’ Behaviors and Welfare Analysis with AI Algorithms: A Review. Revue d' Intelligence Artificielle, 35(3), 243–253. https://hdl.handle.net/2268/263444

  50. Paulo DJ, Rafael GC, Pablo M, de Matheus F, Amália D, Virgínia T (2013) Microalgae classification using semi-supervised and active learning based on Gaussian mixture models. J Braz Comput Soc 19:411–422. https://doi.org/10.1007/s13173-013-0121-y

    Article  MathSciNet  Google Scholar 

  51. Kazi RA, Simu A, Andres M, Christoph S (2021) A simple and robust wetland classification approach by using optical indices, unsupervised and supervised machine learning algorithms. Remote Sensing Appli: Soc Environ 23:2352–9385. https://doi.org/10.1016/j.rsase.2021.100569

    Article  Google Scholar 

  52. Hoora RM, Sheela R (2020) Harvesting Patterns from Textual Web Sources with Tolerance Rough Sets. Patterns 1:100053. https://doi.org/10.1016/j.patter.2020.100053

    Article  Google Scholar 

  53. Balaji TK, Chandra Sekhara RA, Annushree B (2021) Machine learning algorithms for social media analysis: A survey. Computer Science Review. 40: 1574-0137https://doi.org/10.1016/j.cosrev.2021.100395

  54. Kaur A (2020) A Reinforcement Learning based evolutionary multi-objective optimization algorithm for spectrum allocation in Cognitive Radio networks. Phys Commun 43. https://doi.org/10.1016/j.phycom.2020.101196

  55. Stock A, Haupt AJ, Mach ME, Micheli F (2018) Mapping ecological indicators of human impact with statistical and machine learning methods: Tests on the California coast. Eco Inform 48:238–244. https://doi.org/10.1016/j.ecoinf.2018.07.007

    Article  Google Scholar 

  56. Boukaye BT, Bernard KF, Fana T (2018) Deep convolution neural network for image recognition. Eco Inform 48:257–268. https://doi.org/10.1016/j.ecoinf.2018.10.002

    Article  Google Scholar 

  57. Li W, Xiao Y, Song X, Lv Na, Jiang X, Huang Y, Peng J (2021) Chicken Image Segmentation via Multi-Scale Attention-Based Deep Convolutional Neural Network. IEEE Access 9:61398–61407. https://doi.org/10.1109/ACCESS.2021.3074297

    Article  Google Scholar 

  58. Akkem Y, Biswas SK, Varanasi A (2023) Smart farming using artificial intelligence: A review. Eng Appl Artif Intell 120:105899. https://doi.org/10.1016/j.engappai.2023.105899

    Article  Google Scholar 

  59. Thavamani S, Vijayakumar J, Sruthi KB (2023) An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification. Proceedings of Engineering and Technology Innovation. 24, 73–86. https://doi.org/10.46604/peti.2023.11361

Download references

Acknowledgements

This work was supported by the Ministry of Science & Technology, Department of Science and Technology-Delhi. INSPIRE Program at Bharathiar University, TAMIL NADU, Coimbatore-641046 (Ref: DST/INSPIRE Fellowship reference ID: /2019/IF190264).

Author information

Authors and Affiliations

  1. Department of Electronics and Instrumentation, Bharathiar University, Coimbatore, Tamil Nadu, 641046, India

    Thavamani. Subramani, Vijayakumar. Jeganathan & Sruthi. Kunkuma Balasubramanian

Authors
  1. Thavamani. Subramani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vijayakumar. Jeganathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sruthi. Kunkuma Balasubramanian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thavamani. Subramani.

Ethics declarations

Conflict of Interests

The Authors declare they have no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Subramani, T., Jeganathan, V. & Kunkuma Balasubramanian, S. Machine learning and deep learning techniques for poultry tasks management: a review. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18951-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-18951-0

Keywords

Search

Navigation