The agricultural industry still relies heavily on manual labor and relatively little on technology. Automation systems can help and enhance Ethiopia's agricultural sector in a variety of ways. One of them is the utilization of solar energy for the extraction of groundwater and the optimization of irrigation through the use of optimal controllers This thesis discusses the design and simulation research of an automated agricultural system with solar pumping and the best moisture controller. The design process begins with calculating the amount of water needed for a specific piece of land and sizing the solar pumping system as a whole. The optimal controller used is PSO tuned PID controller. It is very well known that PID controllers are linear controllers which are widely used in the industry. They have good performance for linear systems and are easy and cost effective. However, they have an inherent drawback of achieving good performance when the system operating point is changing or the system is nonlinear. In this thesis, the irrigation automation system used brushless DC motor which is linear. The system also has wide variation in operating speed due to the highly nonlinear variation of the moisture content of the soil. Hence the PID controller needs to be tuned properly to give better performance at various operating conditions. Furthermore, the controller also integrates with the solar pumping system so that an optimal harvesting of the solar energy. Due to this the PID parameters are tuned with PSO which is an artificial intelligent-based optimization tool which has shown good performance for other systems. This system is applied on an average farm located in felege birhan which has an average water consumption of 64.8m3/day. 100m deep wells needs 2.38 kw pump 2.81 kw pv. The performance of the controller and overall system is tested through MATLAB simulation. The PSO tuned PID has no overshoot and has settling time 0.3sec less than the auto tuning method of MATLAB. Key words: BLDC motor, solar panel, proportional integral and derivative controller (PID), particle swarm optimization algorithm (PSO).