Design and implementation of an enhanced neuro-fuzzy-based smart system for poultry incubators

This research work entails implementing the design and development of a neuro-fuzzy based smart system using Python. The system learns from historical data of the physical parameters and utilizes fuzzy rules. The system parameters include a NodeMCU controller, temperature and humidity sensors that can measure the conditions of the incubator, C02 sensor and a gyroscope which monitors and adjust the positions of the egg for even distribution of heat around fertilized eggs. It is important that factors necessary for hatching of fertilized egg are kept under appropriate control to increase hatch rate. This was made possible through the actuators which switches the heaters, humidifiers and ventilating fan to moderate environmental The system controller houses an inbuilt Wi-Fi module ESP8266 which enables a remote monitoring and override control when necessary. There is also a provision of a 20X4 LCD monitor for onsite displaying and monitoring of the system status and functionality.