Criteria maximization for water as well as fertilizer management system using neural network and hybrid PID control

Manual fertilization is still used to grow rice, wheat, and maize. Chemical fertilizers come from fertilizer waste and environmental impacts. PID control, which stands for proportional integral derivative, is the main control technique used to regulate agricultural water as well as fertilizer levels. Setting PID control criteria directly affects water as well as fertilizer regulation. The crucial proportionality technique is used to manually create standard PID criteria. This laborious method makes it hard to achieve optimal control effects and costs time. Back propagation (BP) neural network initial weights are optimized using a hybrid maximization technique that combines genetic algorithms with particle swarm maximization. The accurate fertilization management method for farms that use both water and fertilizer in combination uses a hybrid maximization-based BP neural network PID controller to accurately manage fertilizer flow. Thus, the issues indicated may be solved. A microcontroller-based regulator system for accurate fertilizer distribution to large crops and plantations was created and experimentally tested. This combined crop irrigation with fertilizer. The controller has a standard highest overshoot of 5.1% and a normal modification time of 68.99 s, which is greater than the normal PID control techniques based on BP neural network (BP-PID) controllers. Of these, the GA-PSO-BP-PID controller has the best combined control efficiency when applied.