Design of an intelligent poultry disease detection system based on deep learning and IoT

Abstract

This end-of-study project presents the development of an intelligent system for poultry disease detection, combining the Internet of Things (IoT) and Deep Learning. The system uses sensors to collect environmental data (temperature and humidity) and a camera to monitor the poultry in real time, detecting early signs of diseases by analyzing the captured images through Convolutional Neural Network (CNN) algorithm. Additionally, Gradio and Node-RED were utilized to create an interactive Human-Machine Interface (HMI). Gradio provides a user-friendly web interface for visualizing the results from the CNN analysis, while Node-RED is used to display the environmental data and real-time images/videos. This combination of technologies results in a comprehensive and efficient solution for real-time monitoring and management of poultry health