Skip to main content

eWeightSmart - A Smart Approach to Beef Production Management

  • Conference paper
  • First Online:
Smart and Sustainable Agriculture (SSA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1470))

Included in the following conference series:

  • 338 Accesses

Abstract

The use of technologies related to the Internet of Things or Cloud Computing are presently becoming common in different activity sectors. The use of these concepts has allowed to create solutions to solve several social problems, optimizing some of the day-to-day tasks. The agricultural sector is no exception, where increasingly, “smart” solutions begin to emerge in an attempt to reduce the complexity of some tasks or, on a more conceptual level, to make it possible to exploit new markets. Beef production, an agricultural sub-sector, is in many world regions, the main source of income, however, this sub-sector is not always managed in the most optimized way. Cattle body weight management is an important aspect of this sub-sector, providing precious measures for food, health care, breeding and stock selection. For the farmers, the weight measurement when the animal is alive, allows a better commercialization of it, making possible a better management of feeding expenses, reducing waste. It’s therefore necessary to find solutions to ensure a balance between beef production and the associated costs. This paper illustrates an approach to control the evolution of bovine animal’s weight by means of automatic weighing and control of the food amount that is made available to each animal. Using a set of sensors, a mobile platform and using NB-IoT, a communication network, it was possible to devise an approach that can reduce costs in the sector and also enable the exploitation of new business models.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
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. Hocquette, J.-F., Ellies-Oury, M.-P., Lherm, M., Pineau, C., Deblitz, C., Farmer, L.: Current situation and future prospects for beef production in Europe - a review. Asian-Aust. J. Anim. Sci. 31, 1017 (2018)

    Article  Google Scholar 

  2. Ritchie, H., Roser, M.: Meat and dairy production. Our World Data (2017)

    Google Scholar 

  3. Smith, S.B., Gotoh, T., Greenwood, P.L.: Current situation and future prospects for global beef production: overview of special issue. Asian-Aust. J. Anim. Sci. 31, 927 (2018)

    Article  Google Scholar 

  4. Place, S.E., Miller, A.M.: Beef production: what are the human and environmental impacts? Nutr. Today 55(5), 227–233 (2020)

    Article  Google Scholar 

  5. Bronzato, S., Durante, A.: A contemporary review of the relationship between red meat consumption and cardiovascular risk. Int. J. Prevent. Med. 8 (2017)

    Google Scholar 

  6. Pighin, D., et al.: A contribution of beef to human health: a review of the role of the animal production systems. Sci. World J. 2016 (2016)

    Google Scholar 

  7. Gordon, B.L.: Better beef quality drives stability in demand. https://www.beefmagazine.com/beef/better-beef-quality-drives-stability-demand. Accessed 08 May 2021

  8. Koknaroglu, H., Loy, D.D., Wilson, D.E., Hoffman, M.P., Lawrence, J.: Factors affecting beef cattle performance and profitability. Prof. Anim. Sci. 21, 286–296 (2005)

    Article  Google Scholar 

  9. Solanki, A., Nayyar, A.: Green internet of things (G-IoT): ICT technologies, principles, applications, projects, and challenges, pp. 379–405, March 2019

    Google Scholar 

  10. Nayyar, A., Puri, V.: Smart farming: IoT based smart sensors agriculture stick for live temperature and moisture monitoring using arduino, cloud computing & solar technology, pp. 673–680 (2016)

    Google Scholar 

  11. Cows and climate change. https://www.ucdavis.edu/food/news/making-cattle-more-sustainable. Accessed 30 May 2021

  12. Cows, methane, and climate change. https://letstalkscience.ca/educational-resources/stem-in-context/cows-methane-and-climate-change. Accessed 30 May 2021

  13. Vidal, J.: ‘tsunami of data’ could consume one fifth of global electricity by 2025 (2017)

    Google Scholar 

  14. GSA. Narrow band IoT & M2M - global narrowband IoT - LTE-M networks, March 2019. https://gsacom.com/paper/global-narrowband-iot-lte-m-networks-march-2019/. Accessed 01 Mar 2021

  15. GSMA. Mobile iot deployment map. https://www.gsma.com/iot/deployment-map/. Accessed 01 Mar 2021

  16. GSMA. Security Features of LTE-M and NB-IoT Networks (2019). https://www.gsma.com/iot/wp-content/uploads/2019/09/Security-Features-of-LTE-M-and-NB-IoT-Networks.pdf. Accessed 07 Mar 2021

  17. Nayyar, A., Puri, V.: An encyclopedia coverage of compiler’s, programmer’s & simulator’s for 8051, PIC, AVR, ARM, Arduino embedded technologies. Int. J. Reconfigurable Embed. Syst. (IJRES) 5 (2016)

    Google Scholar 

  18. Arduino MKR NB 1500. https://dev.telstra.com/iot-marketplace/arduino-mkr-nb-1500. Accessed 05 June 2021

  19. Mkr family. https://store.arduino.cc/arduino/mkr-family. Accessed 05 June 2021

  20. Outsystems documentation. https://success.outsystems.com/Documentation/11/Developing_an_Application. Accessed 08 May 2021

  21. Payara platform community. https://www.payara.fish/products/payara-platform-community/. Accessed 08 May 2021

  22. GSMA. NarrowBand-Internet of Things (NB-IoT) (2020). https://www.gsma.com/iot/narrow-band-internet-of-things-nb-iot/. Accessed 29 Dec 2020

  23. Arduino.CC. ARDUINO MKR NB 1500 (2020). https://store.arduino.cc/arduino-mkr-nb-1500-1413. Accessed 29 Dec 2020

  24. What is AWS IoT core? https://aws.amazon.com/iot-core/. Accessed 08 May 2021

  25. Pandit, A.: How to use HM-10 BLE module with arduino to control an LED using Android app (2020). https://circuitdigest.com/microcontroller-projects/how-to-use-arduino-and-hm-10-ble-module-to-control-led-with-android-app. Accessed 29 Dec 2020

  26. Get started. https://docs.espressif.com/projects/esp-idf/en/latest/esp32/get-started/. Accessed 08 May 2021

  27. What is a stepper motor? https://learn.adafruit.com/all-about-stepper-motors. Accessed 08 May 2021

  28. Nicole Pontius in Industry Resources. What are RFID Tags? Learn How RFID Tags Work, What They’re Used for, and Some of the Disadvantages of RFID Technology (2020). https://www.camcode.com/asset-tags/what-are-rfid-tags/. Accessed 29 Dec 2020

  29. RFID Reader and Tag - Ultimate Guide on RFID Module (2021). https://www.circuitstoday.com/rfid-reader-tag. Accessed 29 Jan 2020

  30. Pater, S.: How much do your animal weigh (2020). https://cals.arizona.edu/backyards/sites/cals.arizona.edu.backyards/files/p11-12.pdf. Accessed 29 Dec 2020

  31. Rudenko, O., Megel, Y., Bezsonov, O., Rybalka, A.: Cattle breed identification and live weight evaluation on the basis of machine learning and computer vision. In: CMIS, pp. 939–954 (2020)

    Google Scholar 

  32. Zhang, Z.: Microsoft kinect sensor and its effect. IEEE Multimed. 19(2), 4–10 (2012)

    Article  Google Scholar 

  33. Marinello, F., Pezzuolo, A., Donato, C., Gasparini, F., Sartori, L.: Application of kinect-sensor for three-dimensional body measurements of cows, September 2015

    Google Scholar 

  34. Pezzuolo, F.M.A., Sartori, L.: Exploiting low-cost depth cameras for body measurement in the livestock sector (2020). https://ercim-news.ercim.eu/en113/special/exploiting-low-cost-depth-cameras-for-body-measurement-in-the-livestock-sector. Accessed 29 Dec 2020

  35. What is lidar? https://oceanservice.noaa.gov/facts/lidar.html. Accessed 08 May 2021

  36. Huang, L., Li, S., Zhu, A., Fan, X., Zhang, C., Wang, H.: Non-contact body measurement for qinchuan cattle with LiDAR sensor. Sensors 18(9), 3014 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Rui Alves , João Ascensão , Diogo Camelo or Paulo Matos .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Alves, R., Ascensão, J., Camelo, D., Matos, P. (2021). eWeightSmart - A Smart Approach to Beef Production Management. In: Boumerdassi, S., Ghogho, M., Renault, É. (eds) Smart and Sustainable Agriculture. SSA 2021. Communications in Computer and Information Science, vol 1470. Springer, Cham. https://doi.org/10.1007/978-3-030-88259-4_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-88259-4_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88258-7

  • Online ISBN: 978-3-030-88259-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics