Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)

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Comprehensive Analysis of Prevailing Internet of Things (IoT) and Machine Learning (ML) Based Techniques for Cow Disease Detection

Authors
Devinder Kaur1, *, Amandeep Kaur Virk1
1Department of Computer Science, Sri Guru Granth Sahib World University, Fatehgarh Sahib, India
*Corresponding author. Email: devinderkaurcomp@matagujricollege.org
Corresponding Author
Devinder Kaur
Available Online 19 April 2025.
DOI
10.2991/978-94-6463-700-7_11How to use a DOI?
Keywords
Internet of Things (IoT); Machine Learning; Cow Disease Detection; Precision Livestock Farming; Sensor Technology; Predictive Analytics; Animal Health Monitoring
Abstract

The focus of this research paper is on the review of the existing IoT and ML tools and techniques for the detection of diseases in cattle. The dairy and beef industries are largely affected by countless diseases that compromise the health, productivity, and peace of mind for all rearing cows. However, these illnesses need to be prevented and detected as early as possible to protect the herds from any tyrannies and reduce the financial demands of rearing animals. In this study, the use of IoT sensors and ML approaches to design and develop systems for exercising advanced cow health monitoring and control focused on the early stages of potential disease outbreaks has been considered for analysis. Existing research literature is presented, various techniques are reviewed and evaluated, and those disseminated on a particular technique are assessed. The results show that using IoT systems to collect data and ML technologies to analyse it has a lot of potential to change how diseases are found and treated in cows. This could lead to better care for the animals and more efficient animal husbandry.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
Series
Advances in Intelligent Systems Research
Publication Date
19 April 2025
ISBN
978-94-6463-700-7
ISSN
1951-6851
DOI
10.2991/978-94-6463-700-7_11How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Devinder Kaur
AU  - Amandeep Kaur Virk
PY  - 2025
DA  - 2025/04/19
TI  - Comprehensive Analysis of Prevailing Internet of Things (IoT) and Machine Learning (ML) Based Techniques for Cow Disease Detection
BT  - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
PB  - Atlantis Press
SP  - 117
EP  - 136
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-700-7_11
DO  - 10.2991/978-94-6463-700-7_11
ID  - Kaur2025
ER  -