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Smart IoT-Based Wearable Device for Detection and Monitoring of Common Cow Diseases Using a Novel Machine Learning Technique

Rupsa Rani Mishra, D. Chandrasekhar Rao, Ajaya Kumar Tripathy

TL;DR

The paper addresses the challenge of scalable, timely disease detection in large dairy herds by proposing an IoT-based Cyber-Physical System that fuses wearable sensors, cloud preprocessing, and a novel Hyperparameter-Optimized SVM (HPOSVM) classifier. The approach automates data collection, feature extraction, and disease prediction across multiple common bovine diseases, with real-time reporting to stakeholders. Experimental results on real-world data show HPOSVM achieving superior performance (e.g., accuracy around 0.93 and ROC-AUC around 0.96) compared to seven baseline classifiers, validating the method's robustness. The work advances precision livestock farming by delivering low-cost, scalable health monitoring and early intervention capabilities for remote farm operations.

Abstract

Manual observation and monitoring of individual cows for disease detection present significant challenges in large-scale farming operations, as the process is labor-intensive, time-consuming, and prone to reduced accuracy. The reliance on human observation often leads to delays in identifying symptoms, as the sheer number of animals can hinder timely attention to each cow. Consequently, the accuracy and precision of disease detection are significantly compromised, potentially affecting animal health and overall farm productivity. Furthermore, organizing and managing human resources for the manual observation and monitoring of cow health is a complex and economically demanding task. It necessitates the involvement of skilled personnel, thereby contributing to elevated farm maintenance costs and operational inefficiencies. Therefore, the development of an automated, low-cost, and reliable smart system is essential to address these challenges effectively. Although several studies have been conducted in this domain, very few have simultaneously considered the detection of multiple common diseases with high prediction accuracy. However, advancements in Internet of Things (IoT), Machine Learning (ML), and Cyber-Physical Systems have enabled the automation of cow health monitoring with enhanced accuracy and reduced operational costs. This study proposes an IoT-enabled Cyber-Physical System framework designed to monitor the daily activities and health status of cow. A novel ML algorithm is proposed for the diagnosis of common cow diseases using collected physiological and behavioral data. The algorithm is designed to predict multiple diseases by analyzing a comprehensive set of recorded physiological and behavioral features, enabling accurate and efficient health assessment.

Smart IoT-Based Wearable Device for Detection and Monitoring of Common Cow Diseases Using a Novel Machine Learning Technique

TL;DR

The paper addresses the challenge of scalable, timely disease detection in large dairy herds by proposing an IoT-based Cyber-Physical System that fuses wearable sensors, cloud preprocessing, and a novel Hyperparameter-Optimized SVM (HPOSVM) classifier. The approach automates data collection, feature extraction, and disease prediction across multiple common bovine diseases, with real-time reporting to stakeholders. Experimental results on real-world data show HPOSVM achieving superior performance (e.g., accuracy around 0.93 and ROC-AUC around 0.96) compared to seven baseline classifiers, validating the method's robustness. The work advances precision livestock farming by delivering low-cost, scalable health monitoring and early intervention capabilities for remote farm operations.

Abstract

Manual observation and monitoring of individual cows for disease detection present significant challenges in large-scale farming operations, as the process is labor-intensive, time-consuming, and prone to reduced accuracy. The reliance on human observation often leads to delays in identifying symptoms, as the sheer number of animals can hinder timely attention to each cow. Consequently, the accuracy and precision of disease detection are significantly compromised, potentially affecting animal health and overall farm productivity. Furthermore, organizing and managing human resources for the manual observation and monitoring of cow health is a complex and economically demanding task. It necessitates the involvement of skilled personnel, thereby contributing to elevated farm maintenance costs and operational inefficiencies. Therefore, the development of an automated, low-cost, and reliable smart system is essential to address these challenges effectively. Although several studies have been conducted in this domain, very few have simultaneously considered the detection of multiple common diseases with high prediction accuracy. However, advancements in Internet of Things (IoT), Machine Learning (ML), and Cyber-Physical Systems have enabled the automation of cow health monitoring with enhanced accuracy and reduced operational costs. This study proposes an IoT-enabled Cyber-Physical System framework designed to monitor the daily activities and health status of cow. A novel ML algorithm is proposed for the diagnosis of common cow diseases using collected physiological and behavioral data. The algorithm is designed to predict multiple diseases by analyzing a comprehensive set of recorded physiological and behavioral features, enabling accurate and efficient health assessment.
Paper Structure (11 sections, 14 equations, 7 figures, 2 tables)

This paper contains 11 sections, 14 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. A Cyber-Physical System for Personalized Health Monitoring in Dairy Cows
  3. Wearable Sensing Model for Recording Physical Parameters
  4. Hardware Architecture and Sensor Integration
  5. Signal Communication
  6. Preprocessing and Feature Generation
  7. Cow Disease Prediction
  8. Real-Time Reporting
  9. HPOSVM: A Hyperparameter-Optimized Support Vector Machine for Early Detection of Common Cow Diseases
  10. Experimental setup and Performance Evaluation
  11. Conclusion and Future Work

Figures (7)

  • Figure 1: Smart Dairy Cow Farm Cyber-Physical System.
  • Figure 2: Overview of the working model of the proposed CPS.
  • Figure 3: Architecture of the proposed cyber-physical system (CPS) for automated cow health monitoring.
  • Figure 4: ESP32 data transmission via Wi-Fi, Cellular, or LoRa/BLE Gateway to the cloud.
  • Figure 5: Performance metrics (Accuracy, Precision, Recall, F1 Score) across training data percentages from 1% to 70%.
  • ...and 2 more figures