IOT Driven Bovine Vitalis

EasyChair Preprint no. 13630

Abstract

In today's burgeoning global economy, dairy farming has emerged as a pivotal and daily necessity. The productivity of dairy products is intricately tied to the health of the cattle. Even a minor ailment affecting one animal within the herd has the potential to cascade, impacting the entire group and compromising their overall productivity. Recognizing the critical relationship between animal health and dairy output, an innovative Internet of Things (IoT)-driven system has been implemented. This advanced system utilizes sensors to collect data, enabling the early prediction of diseases through the processing of symptoms using supervised machine learning algorithms.

The significance of early disease prediction extends beyond mitigating disease outbreaks; it empowers dairy farmers to make well-informed decisions that contribute to the establishment of sustainable farming practices. The primary objective is to promptly administer the right treatment to cattle, safeguarding them from adverse effects on milk production and the quality of the produced milk. Given the challenge of individually identifying and monitoring each animal in a cattle herd, often comprising a substantial number of individuals, the system adopts a unique approach. Each animal is assigned a distinct identifier, allowing for the separate monitoring of the diagnosis and treatment of each. This meticulous and personalized approach not only facilitates faster treatment but also contributes to the earlier cure of diseases, ultimately enhancing overall production efficiency in the dairy farming industry.

Keyphrases: Algorithms, Dairy farming, Disease Prediction, IoT, machine learning, Supervised Machine Learning.

@Booklet{EasyChair:13630,
  author = {Sai Dedipya Gonuguntla and Sai Charan Edupuganti and Govind Rajesh Tallada and Pavan Kumar Chitturi},
  title = {IOT Driven Bovine Vitalis},
  howpublished = {EasyChair Preprint no. 13630},

  year = {EasyChair, 2024}}