IoT Based Poultry Farm Monitoring and Disease Detection System

Abstract

The “IoT Based Poultry Monitoring and Disease Detection System” is an innovative and intelligent solution designed to enhance poultry farm monitoring through the integration of IoT (Internet of Things), robotics, and wireless communication technologies. This project introduces a Raspberry Pi-based robotic surveillance unit that moves autonomously under the poultry area to monitor bird health and environmental conditions. Traditional poultry farms rely heavily on manual inspection and human supervision, which are time-consuming, labour-intensive, and prone to human error. To overcome these limitations, the proposed system employs a mobile robot equipped with multiple. In addition to visual monitoring, the system is capable of detecting harmful gases, such as ammonia (NH?) and carbon dioxide (CO?), which often accumulate in closed poultry environments and pose serious health risks to the birds. A gas sensor installed on the robot continuously measures air quality, and when gas exceed safe limits, the system automatically triggers an alert. All collected data, including gas readings, disease alerts, and camera feed information, is transferred from the poultry area to the main monitoring room using LoRa (Long Range) communication, ensuring efficient and reliable data transmission over long distances with minimal power consumption. The ESP32 module assists in data communication and interface control for peripheral devices, while a 16×2 LCD display shows real-time data locally. Furthermore, the Raspberry Pi acts as the central processing and control unit, hosting a web-based dashboard that displays live video, gas level graphs, and disease detection alerts. This enables remote access from any device connected to the internet, providing farm owners with a comprehensive, real-time overview of their poultry environment.

Introduction

The proposed Smart Poultry Management System is an automated, IoT-based solution designed to improve poultry health monitoring and farm productivity. Traditional poultry farming relies on manual inspection of birds and environmental conditions, which is labor-intensive, prone to errors, and often leads to delayed detection of diseases and hazardous conditions. These shortcomings can result in high bird mortality, reduced productivity, and financial losses.

To address these challenges, the system integrates Raspberry Pi, robotic automation, IoT sensors, LoRa communication, and AI-based monitoring into a single intelligent platform. A mobile robotic unit autonomously navigates poultry sheds using IR sensors for line following and ultrasonic sensors for obstacle detection. The robot continuously collects environmental data, including temperature, humidity, and harmful gas concentrations (ammonia, methane, and carbon dioxide), using DHT22 and MQ-series gas sensors. A Raspberry Pi camera captures real-time video for remote surveillance and disease detection through computer vision techniques.

The collected data is processed locally and transmitted over long distances using LoRa communication, making the system suitable for rural areas with limited internet connectivity. A web-based dashboard developed using Flask displays live video streams, environmental readings, historical data, and automated alerts. The system can identify diseased, inactive, or injured birds and notify farm managers when abnormal conditions are detected.

Compared with existing Environmentally Controlled (EC) poultry sheds, the proposed system overcomes limitations such as lack of disease detection, absence of gas monitoring, fixed sensor blind spots, and high installation costs. The mobile robotic platform provides close-range inspection and flexible monitoring throughout the poultry house.

Future enhancements include integrating AI and machine learning for disease prediction, advanced sensors such as thermal cameras and RFID tags, automated disease diagnosis linked to veterinary databases, and full automation of ventilation, feeding, lighting, and water management systems. Overall, the proposed solution offers an affordable, intelligent, and scalable approach for modern poultry farming, improving animal welfare, reducing losses, and supporting sustainable agricultural practices.

Conclusion

The IoT Based Poultry Management and Disease Detection System successfully bring together sensing, automation, LoRa communication, and AI-based analysis to create a reliable and continuous monitoring solution for poultry farms. By integrating temperature, humidity, ammonia, and moisture sensors with a mobile robotic platform and a Raspberry Pi camera, the system is capable of observing both environmental conditions and poultry behaviour in real time. The use of lightweight AI techniques helps detect abnormal patterns such as inactivity, clustering, or signs of stress, enabling early identification of potential disease conditions that often go unnoticed during manual inspection. The inclusion of LoRa communication ensures long-range, low-power, and uninterrupted data transmission from within the shed to the control room, making the system suitable even for large poultry houses where Wi-Fi networks are unreliable. The decision-making and alert module provides timely notifications to farmers, allowing immediate corrective action to be taken in critical situations. All events, sensor readings, and snapshots are logged for future reference, improving farm management and supporting data-driven health analysis. Overall, the developed system reduces manual workload, improves accuracy, and enhances the safety and wellbeing of the flock. By combining IoT, robotics, and edge-level intelligence, the project demonstrates a practical, scalable, and cost-effective approach for modernizing poultry farm operations. The outcomes align closely with recent advancements in smart farming research and show strong potential for future expansion through more advanced AI models, multi-shed integration, and enhanced automation features.

References

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Copyright

Copyright © 2026 Mr. Krushna Khillari, Mr. Satyam Navale, Mr. Karan Mandlik. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.