This study is on improvement of performance of the chicken Banda, using indoor change in environmental conditions for temperature control. The differential change in climatic conditions is technically used to put on the fan and the Banda so as to realize the right comfortable indoor conditions. The chicken chicks’ Banda Mathematical model is created, prototype designed, temperature controller to depict a two systems simulation of neuro fuzzy logic and fuzzy logic .The performance is analyzed by the use of Matlab Simulink latest edition. To monitor the temperature of the Chicken cage the neural fuzzy logic technique is utilized. As far as the prototype is concerned the chicks’ cage set temperature is fixed at 26.50C. The study will show that the reference input can be kept on track by the process controller hence proving the principle that the neural fuzzy control is much superior in optimizing performance compared to the fuzzy only controllers. The Back propagation (BP) and least square estimator (LSE) are the hybrid optimization methods which are used. For data training the gradient descent method (GDM) is used. The research reveal that  there is drastic performance improvement in the behavior response where  result show that there settling time is reduced from 0.75 to 0.48 seconds while the percentage  overshoot is also reduced down  from 29.9% to 0.9345%.

The research is about developing of prototype Humidity control unit of a chicken chick Banda for maximum reduction in energy wastage and ensuring conducive environmental condition for bird’s growth and development using the proportional integral differential (PID) controller and the particle swarm optimization (PSO) technique for comparison purposes. The PSO stated here in is a stochastic optimization method working on the movement of swarm so as to achieve convergence. The study is achieved through designing of a prototype of the humidity environment controller to achieve two states or conditions that is for the controlled case and for the uncontrolled case. Environmental humidity control is achieved using a programmed Arduino and the DC FAN. The process is then designed using the MATLAB simulation software operating at the Simulink model designing platform. The same design is connected to the PID controller and then also tuned using the PID tuning platform on the Matlab. The same design is implemented on the workspace using particle swarm optimization method and it is then run to see the system behavior in terms of settling time, rising time and peak overshoot. The major reason of the study is to demystify the myth that one can only use conventional PID controller techniques in performance improvement and that there is a better method which can similarly be used with better results and cheaper. Most poultry farmers are stack with their old ways of achieving good performance therefore the results of this work will be an eye opener for them to embrace new techniques in the market The presented particle swarm optimization techniques shows impressive performance in terms of the settling time, rise time and over shoot.