Transforming Business through Sensors, Big Data, and Machine Learning in ModernAnimal Farming

Authors

  • Muhammad Irfan Haqnawaz MNS University of Agriculture Multan
  • khadija Altaf
  • Muhammad Qayyum MNS University of Agriculture, Multan, Pakistan.
  • Muhammad Akhter MNS University of Agriculture, Multan, Pakistan.
  • Salman Muhammad MNS University of Agriculture, Multan, Pakistan.
  • Hira Somra MNS University of Agriculture, Multan, Pakistan.
  • Muntazir Mehdi Fazaia Bilquis College of Education for Women, Rawalpindi, Pakistan

Keywords:

Cardiac disease; intelligent model; disease prediction; deep learning

Abstract

When it comes to animal production, humans have always depended on gut feelings, common knowledge, and sensory inputs, even when domesticating animals started thousands of years ago. Our achievements in farming and animal husbandry have been substantial thus far thanks to this. More centralised, large-scale, and efficient animal farming may be possible as a result of both the increasing demand for food and the development of sensing technologies. As we know it, it could revolutionise animal husbandry. This study takes a high-level look at the possibilities and threats that sensor technology pose to animal producers’ ability to increase their output of meat and other animal products. The purpose of this study is to investigate how sensors, big data, artificial intelligence, and machine learning may assist animal producers in improving animal comfort, increasing productivity per hectare, decreasing production costs, and increasing efficiency. It delves into the difficulties and restrictions of technology as well. This study explores the many uses of animal farming technology in order to comprehend its worth in assisting farmers in bettering the health of their animals, increasing their profitability, and decreasing their impact on the environment.

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Published

16-02-2024

How to Cite

Transforming Business through Sensors, Big Data, and Machine Learning in ModernAnimal Farming. (2024). International Journal of Computational and Innovative Sciences, 2(4), 38-55. http://ijcis.com/index.php/IJCIS/article/view/98

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