Skip to main content
SpringerLink
Log in

An IoT Ecosystem-Based Architecture of a Smart Livestock Farm

  • Conference paper
  • First Online:
Advances in Machine Intelligence and Computer Science Applications (ICMICSA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 656))

  • 256 Accesses

Abstract

Livestock farm managers and farmers must adopt new methods to boost their productivity and meet the growing demand for food. In this regard comes this work to offer an IoT ecosystem-based architecture of a smart livestock farm that aims to help farmers to improve their farm performance and save a lot of waste and prevent economic losses. The architecture that can be applied in cattle, small ruminants and poultry farms focuses on the continuous monitoring of livestock health, performance, reproduction, feed, milking, and behavior alongside identification and location tracking, all through advanced technologies like the Internet of Things, Artificial Intelligence and Big data and using IoT sensors and other smart devices.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Calicioglu, O., Flammini, A., Bracco, S., Bellù, L., Sims, R.: The future challenges of food and agriculture: an integrated analysis of trends and solutions. Sustainability 11(1), 222 (2019)

    Article  Google Scholar 

  2. Liu, Y., Ma, X., Shu, L., Hancke, G.P., Abu-Mahfouz, A.M.: From industry 4.0 to agriculture 4.0: current status, enabling technologies, and research challenges. IEEE Trans. Ind. Inf. 17(6), 4322–4334 (2020)

    Article  Google Scholar 

  3. Neethirajan, S.: The role of sensors, big data and machine learning in modern animal farming. Sens. Bio-Sensing Res. 29, 100367 (2020)

    Article  Google Scholar 

  4. Akhigbe, B.I., Munir, K., Akinade, O., Akanbi, L., Oyedele, L.O.: IoT technologies for livestock management: a review of present status, opportunities, and future trends. Big Data Cogn. Comput. 2021(5), 10 (2021)

    Article  Google Scholar 

  5. Gope, P., Gheraibia, Y., Kabir, S., Sikdar, B.: A secure IoT-based modern healthcare system with fault-tolerant decision making process. IEEE J. Biomed. Health Inf. 25(3), 862–873 (2020)

    Article  Google Scholar 

  6. Morrone, S., Dimauro, C., Gambella, F., Cappai, M.G.: Industry 4.0 and precision livestock farming (PLF): an up to date overview across animal productions. Sensors 22(12), 4319 (2022)

    Article  Google Scholar 

  7. Mandel, R., Harazy, H., Gygax, L., Nicol, C.J., Ben-David, A., Whay, H.R., Klement, E.: Detection of lameness in dairy cows using a grooming device. J. Dairy Sci. 101(2), 1511–1517 (2018)

    Article  Google Scholar 

  8. Grilli, G., Borgonovo, F., Tullo, E., Fontana, I., Guarino, M., Ferrante, V.: A pilot study to detect coccidiosis in poultry farms at early stage from air analysis. Biosyst. Eng. 173, 64–70 (2018)

    Article  Google Scholar 

  9. Carpentier, L., et al.: Automatic cough detection for bovine respiratory disease in a calf house. Biosyst. Eng. 173, 45–56 (2018)

    Article  Google Scholar 

  10. Mahdavian, A., Minaei, S., Yang, C., Almasganj, F., Rahimi, S., Marchetto, P.M.: Ability evaluation of a voice activity detection algorithm in bioacoustics: a case study on poultry calls. Comput. Electron. Agric. 168, 105100 (2020)

    Article  Google Scholar 

  11. Lowe, G., Sutherland, M., Waas, J., Schaefer, A., Cox, N., Stewart, M.: Infrared thermography-a non-invasive method of measuring respiration rate in calves. Animals 9(8), 535 (2019)

    Article  Google Scholar 

  12. Gelasakis, A.I., et al.: Evaluation of infrared thermography for the detection of footrot and white line disease lesions in dairy sheep. Vet. Sci. 8(10), 219 (2021)

    Article  Google Scholar 

  13. LeRoy, C.: Estrus detection intensity and accuracy, and optimal timing of insemination with automated activity monitors for dairy cows (Doctoral dissertation, University of Guelph) (2016)

    Google Scholar 

  14. Marquez, H.P., Ambrose, D.J., Schaefer, A.L., Cook, N.J., Bench, C.J.: Infrared thermography and behavioral biometrics associated with estrus indicators and ovulation in estrus-synchronized dairy cows housed in tiestalls. J. Dairy Sci. 102(5), 4427–4440 (2019)

    Article  Google Scholar 

  15. De Freitas, A.C.B., et al.: Surface temperature of ewes during estrous cycle measured by infrared thermography. Theriogenology 119, 245–251 (2018)

    Article  Google Scholar 

  16. Ikurior, S.J., Marquetoux, N., Leu, S.T., Corner-Thomas, R.A., Scott, I., Pomroy, W.E.: What are sheep doing? Tri-axial accelerometer sensor data identify the diel activity pattern of ewe lambs on pasture. Sensors 21(20), 6816 (2021)

    Article  Google Scholar 

  17. Ruuska, S., Kajava, S., Mughal, M., Zehner, N., Mononen, J.: Validation of a pressure sensor-based system for measuring eating, rumination and drinking behaviour of dairy cattle. Appl. Animal Behav. Sci. 174, 19–23 (2016)

    Article  Google Scholar 

  18. Werner, J., et al.: Evaluation of the RumiWatchSystem for measuring grazing behaviour of cows. J. Neurosci. Methods 300, 138–146 (2018)

    Article  Google Scholar 

  19. Batuto, A., Dejeron, T.B., Cruz, P.D., Samonte, M.J.C.: e-poultry: an IoT poultry management system for small farms. In: 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA) (pp. 738–742). IEEE (2020)

    Google Scholar 

  20. Bonora, F., Benni, S., Barbaresi, A., Tassinari, P., Torreggiani, D.: A cluster-graph model for herd characterisation in dairy farms equipped with an automatic milking system. Biosyst. Eng. 167, 1–7 (2018)

    Article  Google Scholar 

  21. Benni, S., Pastell, M., Bonora, F., Tassinari, P., Torreggiani, D.: A generalised additive model to characterise dairy cows’ responses to heat stress. animal, 14(2), 418-424 (2020)

    Google Scholar 

  22. Mozo, R., Alabart, J.L., Rivas, E., Folch, J.: New method to automatically evaluate the sexual activity of the ram based on accelerometer records. Small Ruminant Res. 172, 16–22 (2019)

    Article  Google Scholar 

  23. Alejandro, M.: Automation devices in sheep and goat machine milking. Small Ruminant Res. 142, 48–50 (2016)

    Article  Google Scholar 

  24. Bovo, M., Agrusti, M., Benni, S., Torreggiani, D., Tassinari, P.: Random forest modelling of milk yield of dairy cows under heat stress conditions. Animals 11(5), 1305 (2021)

    Article  Google Scholar 

  25. Meunier, B., Pradel, P., Sloth, K.H., Cirié, C., Delval, E., Mialon, M.M., Veissier, I.: Image analysis to refine measurements of dairy cow behaviour from a real-time location system. Biosyst. Eng. 173, 32–44 (2018)

    Article  Google Scholar 

  26. Qiao, Y., Su, D., Kong, H., Sukkarieh, S., Lomax, S., Clark, C.: BiLSTM-based individual cattle identification for automated precision livestock farming. In: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) (pp. 967-972). IEEE (2020)

    Google Scholar 

  27. Carslake, C., Vázquez-Diosdado, J.A., Kaler, J.: Machine learning algorithms to classify and quantify multiple behaviours in dairy calves using a sensor: moving beyond classification in precision livestock. Sensors 21(1), 88 (2020)

    Article  Google Scholar 

  28. Unold, O., et al.: IoT-based cow health monitoring system. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12141, pp. 344–356. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50426-7_26

    Chapter  Google Scholar 

  29. Montalcini, C.M., Voelkl, B., Gómez, Y., Gantner, M., Toscano, M.J.: Evaluation of an active LF tracking system and data processing methods for livestock precision farming in the poultry sector. Sensors 22(2), 659 (2022)

    Article  Google Scholar 

  30. Bishop, J., Falzon, G., Trotter, M., Kwan, P., Meek, P.: Sound analysis and detection, and the potential for precision livestock farming-a sheep vocalization case study. In: Proceedings of the 1st Asian-Australasian Conference on Precision Pastures and Livestock Farming, Hamilton-New Zealand (pp. 1-7) (2017)

    Google Scholar 

  31. Cui, Y., Zhang, M., Li, J., Luo, H., Zhang, X., Fu, Z.: WSMS: Wearable stress monitoring system based on IoT multi-sensor platform for living sheep transportation. Electronics 8(4), 441 (2019)

    Article  Google Scholar 

  32. El Moutaouakil, K., Jabir, B., Falih, N.: A convolutional neural networks-based approach for potato disease classification. In: International Conference on Business Intelligence (pp. 29-40). Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06458-6_2

  33. Rabhi, L., Falih, N., Afraites, A., Bouikhalene, B.: Big data approach and its applications in various fields. Procedia Comput. Sci. 155, 599–605 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LIMATI Laboratory, Polydisciplinary Faculty, University of Sultan Moulay Slimane, Mghila, BP 592, Beni Mellal, Morocco

    Khalid El Moutaouakil, Hamza Jdi, Brahim Jabir & Noureddine Falih

Authors
  1. Khalid El Moutaouakil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamza Jdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Brahim Jabir
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Noureddine Falih
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khalid El Moutaouakil .

Editor information

Editors and Affiliations

  1. National School of Applied Sciences of Khouribga, Sultan Moulay Slimane University, Khouribga, Morocco

    Noureddine Aboutabit

  2. ENSIAS, Mohammed V University, Rabat, Morocco

    Mohamed Lazaar

  3. National School of Applied Sciences of Khouribga, Sultan Moulay Slimane University, Khouribga, Morocco

    Imad Hafidi

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

El Moutaouakil, K., Jdi, H., Jabir, B., Falih, N. (2023). An IoT Ecosystem-Based Architecture of a Smart Livestock Farm. In: Aboutabit, N., Lazaar, M., Hafidi, I. (eds) Advances in Machine Intelligence and Computer Science Applications. ICMICSA 2022. Lecture Notes in Networks and Systems, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-031-29313-9_25

Download citation

Publish with us

Policies and ethics

Search

Navigation