Quadcopter technology could help farmers around the world to monitor their agriculture tornknow accurate and up-to-date information quickly on the health of their crops and the environmentalrncondition of the land. This thesis addresses particle swarm optimization tunedrnfractional-order sliding mode controller for altitude stabilization and trajectory tracking ofrnagricultural monitoring UAV. First, the quadrotor dynamics are modeled using the Newton-rnEuler systems approach. Second, Fractional-order (FO) sliding mode control (SMC) systemrnis developed for attitude and position trajectory tracking of a quadrotor unmanned aerialrnvehicle (UAV) system under unknown external disturbance. Quadcopter control algorism isrndivided into inner and outer control loops because the quadcopter is under actuated system,rnWhere direct control of all six degrees of freedom is not possible. The outer loop controlsrnthe altitude and generates roll and pitch angle reference trajectories controlled in the innerrnloop. The inner loop controls attitude (roll, pitch, and yaw) of the quadcopter.rnIn order to achieve good task performance for agility, flying efficiency and trajectory tracking,rnparticle swarm optimization (PSO) algorithms are used to obtain parameters of fractionalrnorder sliding mode controller (FOSMC).rnThe comparison of conventional sliding mode control with fractional-order sliding mode controllerrnis analyzed. The effectiveness of the proposed control scheme is verified by developingrnsimulation results for the quadcopter study in MATLAB/SIMULINK software. In this work,rndifferent types of tasks are performed under different conditions. Indeed, the ability of thernproposed controller to track the imposed trajectories and achieving of position in space isrnwell seen from 3D path tracking simulations and even in presence of disturbances