Skip to main content
SpringerLink
Log in

Can Machine Learning Algorithms Improve Dairy Management?

  • Conference paper
  • First Online:
Machine Learning and Computational Intelligence Techniques for Data Engineering (MISP 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 998))

Included in the following conference series:

  • 280 Accesses

Abstract

Technology adoption in the Indian dairy industry is significantly less than in other sectors. There is a need for technology adoption in dairy farming like artificial intelligence (AI), machine learning (ML), and the internet of things (IoT). Manual handling in dairy operations is becoming a tough job, reducing the efficiency in milk production. This paper aims to determine whether the dairy industry can adopt ML techniques for better milk production. This paper applied supervised ML techniques to improve dairy management. The data was recorded and collected from smart devices that the cow wears. Those data can be integrated by ordering from the innovative wearable database and analyzed by using supervised machine learning algorithms like SVM, bagging and boosting techniques, K-nearest neighbor and hybridization techniques, which classifies the feature of the data with at most accuracy and can provide ultimate results to predict the nature of a herd of cows for the best production and the evaluation can be done by calculating precision, sensitive, recall, f-Measure and accuracy.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Morota G, Ventura RV, Silva FF, Koyama M, Fernando SC (2018) Big data analytics and precision animal agriculture symposium: machine learning and data mining advance big data analysis in precision animal agriculture. J Anim Sci

    Google Scholar 

  2. Nayeri S, Sargolzaei M, Tulpan D (2019) A review of traditional and machine learning methods applied to animal breeding. Anim Health Res Rev

    Google Scholar 

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

    Google Scholar 

  4. Gálik R, Lüttmerding G, Boďo Š, Knížková I, Kunc P (2021) Impact of heat stress on selected parameters of robotic milking. Animals

    Google Scholar 

  5. Mukherjee S, Chittipaka V (2021) Analysing the adoption of intelligent agent technology in food supply chain management: empirical evidence. FIIB Bus Rev

    Google Scholar 

  6. Shorten PR (2021) Computer vision and weigh scale-based prediction of milk yield and udder traits for individual cows. Comput Electron Agric

    Google Scholar 

  7. Contla Hernández B, Lopez-Villalobos N, Vignes M (2021) Identifying health status in grazing dairy cows from milk mid-infrared spectroscopy by using machine learning methods. Animals

    Google Scholar 

  8. Ferris MC, Christensen A, Wangen SR (2020) Symposium review: dairy brain—Informing decisions on dairy farms using data analytics. J Dairy Sci

    Google Scholar 

  9. Warner D, Vasseur E, Lefebvre DM, Lacroix R (2020) A machine learning based decision aid for lameness in dairy herds using farm-based records. Comput Electron Agric

    Google Scholar 

  10. Datasets. https://www.kaggle.com/datasets?search=animal

  11. Satoła A, Bauer EA (2021) Predicting subclinical ketosis in dairy cows using machine learning techniques. Animals

    Google Scholar 

  12. Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D (2021) Machine learning in agriculture: a comprehensive updated review. Sensors

    Google Scholar 

  13. Luo Y, Zeng Z, Lu H, Lv E (2021) Posture detection of individual pigs based on lightweight convolution neural networks and efficient channel-wise attention. Sensors

    Google Scholar 

  14. Shine P, Murphy MD (2022) Over 20 years of machine learning applications on dairy farms: a comprehensive mapping study. Sensors

    Google Scholar 

  15. Chugh A, Makkar P, Aggarwal S, Sharma S, Singh YK (2020) Approach of image processing in diagnosis and medication of fungal infections in pet animals. Int J Innov Res Comput Sci Technol (IJIRCST)

    Google Scholar 

  16. Cockburn M (2020) Application and prospective discussion of machine learning for the management of dairy farms. Animals

    Google Scholar 

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

    Google Scholar 

  18. Balasso P, Marchesini G, Ughelini N, Serva L, Andrighetto I (2021) Machine learning to detect posture and behavior in dairy cows: information from an accelerometer on the animal’s left flank. Animals

    Google Scholar 

  19. Mwanga G, Lockwood S, Mujibi DF, Yonah Z, Chagunda MG (2020) Machine learning models for predicting the use of different animal breeding services in smallholder dairy farms in Sub-Saharan Africa. Trop Anim Health Prod

    Google Scholar 

  20. Soysal Y, Ayhan Z, Eştürk O, Arıkan MF (2009) Intermittent microwave–convective drying of red pepper: drying kinetics, physical (colour and texture) and sensory quality. Biosyst Eng

    Google Scholar 

  21. Fadul M, Bogdahn C, Alsaaod M, Hüsler J, Starke A, Steiner A, Hirsbrunner G (2017) Prediction of calving time in dairy cattle. Anim Reprod Sci

    Google Scholar 

  22. Sedighi T, Varga L (2021) Evaluating the Bovine tuberculosis eradication mechanism and its risk factors in England’s cattle farms. Int J Environ Res Public Health

    Google Scholar 

  23. Tarjan L, Šenk I, Pracner D, Rajković D, Štrbac L (2021) Possibilities for applying machine learning in dairy cattle breeding. In: 2021 20th international symposium INFOTEH-JAHORINA (INFOTEH)

    Google Scholar 

  24. Tassinari P, Bovo M, Benni S, Franzoni S, Poggi M, Mammi LM, Mattoccia S, Di Stefano L, Bonora F, Barbaresi A, Santolini E (2021) A computer vision approach based on deep learning for the detection of dairy cows in free stall barn. Comput Electron Agric

    Google Scholar 

  25. Dev RD, Badhan Ak, Roy R (2020) A study of artificial emotional intelligence for human—Robot interaction. J Crit Rev

    Google Scholar 

  26. Garcia R, Aguilar J, Toro M, Pinto A, Rodriguez P (2020) A systematic literature review on the use of machine learning in precision livestock farming. Comput Electron Agric

    Google Scholar 

  27. Roy R, Dev DR, Prasad VS (2020) Socially intelligent robots: evolution of human-computer interaction. J Crit Rev

    Google Scholar 

  28. Mukherjee S, Baral MM, Venkataiah C, Pal SK, Nagariya R (2021) Service robots are an option for contactless services due to the COVID-19 pandemic in the hotels. Decision

    Google Scholar 

  29. Cioffi R, Travaglioni M, Piscitelli G, Petrillo A, De Felice F (2020) Artificial intelligence and machine learning applications in smart production: progress, trends, and directions. Sustainability

    Google Scholar 

  30. Nosratabadi S, Ardabili S, Lakner Z, Mako C, Mosavi A (2021) Prediction of food production using machine learning algorithms of multilayer perceptron and ANFIS. Agriculture

    Google Scholar 

  31. Roy R, Giduturi A (2019) Survey on pre-processing web log files in web usage mining. Int J Adv Sci Technol

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, India

    Rita Roy

  2. Department of Computer Science, Lovely Professional University, Phagwara, Punjab, India

    Ajay Kumar Badhan

Authors
  1. Rita Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ajay Kumar Badhan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rita Roy .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh, India

    Pradeep Singh

  2. Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh, India

    Deepak Singh

  3. Department of Computer Science and Engineering, International Institute of Information Technology, Naya Raipur, Chhattisgarh, India

    Vivek Tiwari

  4. Østfold University College, Halden, Norway

    Sanjay Misra

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Roy, R., Badhan, A.K. (2023). Can Machine Learning Algorithms Improve Dairy Management?. In: Singh, P., Singh, D., Tiwari, V., Misra, S. (eds) Machine Learning and Computational Intelligence Techniques for Data Engineering. MISP 2022. Lecture Notes in Electrical Engineering, vol 998. Springer, Singapore. https://doi.org/10.1007/978-981-99-0047-3_33

Download citation

Publish with us

Policies and ethics

Search

Navigation