IoT based Poultry Form Monitoring System with Deep Learning Techniques

The existing poultry farms are manual, focusing on temperature, humidity air quality, illumination, ventilation, and food feeding. On the other hand, the high grill chicken fatality rate is a significant issue in almost all the poultry farms. To improve productivity, this research study aims to automate the monitoring and maintenance using IoT and Wireless Sensor Network (WSN). The goal of this research work is to produce healthy chickens and reduce mortality rates, highlighting recent advancements in hardware and software components. This will help to improve productivity in Brunei poultry farm and reduce the chicken mortality rate. This research work aims to improve quality of chicken technology using IoT and WSN technologies. A prototype was built, and parameters were checked against threshold levels. Corrective actions are initiated when parameters exceed thresholds in order to reduce the chicken fatality rates. An attention encoder structure is also suggested to increase the detection accuracy. The final findings show that the DenseNet can improve chicken maturity identification accuracy by implementing this structure. Also, the proposed system automatically alerts the users.