## 4 Results and Discussion
Control engineering knowledge and experience are needed for this task. However, the system's overall performance was smooth when using a fuzzy PID controller for drone orientation. Figure 7:0 Position controller with noise as the input. Figure 7:1 Orientation controller with noise as the input. This is because fuzzy PID controllers have a number of benefits over conventional control systems, such as efficiency, adaptability, and resilience. Figure 14 shows the agricultural drone's velocity response. The agricultural drone that has a PID controller exhibits cyclical stability in its velocity, while the drone outfitted with a fuzzy PID controller displays steady velocity. The agricultural drone's velocity may be readily adjusted to various operating conditions thanks to its fuzzy PID controller, which also requires less processing. These traits are common to fuzzy logic controllers. The agricultural drone's velocity with the fuzzy PID controller has a smooth form, it is quick to achieve its maximum points, it overshoots, it settles quickly, and it may readily obtain a steady-state error. The agricultural drone's PID controller-equipped velocity has a less smooth shape and is cyclically stable. Because PID controllers do not specifically take into account system constraints, including input or output limits, breaking these constraints could result in unfavorable behavior. The fuzzy PID technique balanced this controller effort. Because of this, fuzzy PID controllers can perform better in control than conventional PID controllers, particularly in systems that have uncertainties and non-linearities. Figure 15 displays the inaccuracy. This was calculated by deducting the performance of the PID controller from that of the FPID controller. The position error increases positively and is smooth, because an agricultural drone's position is affected by both PID and FPID controllers in the same way. On the other hand, orientation issues are a talking point, since the position of the agricultural drone indicates that the FPID and PID controllers have different effects. The agricultural drone's ability to follow the course is evaluated once the controller design is complete. Given that a device labeled as an agricultural drone path tracking controller assists drones in following an established path, the agricultural drone's position and orientation are usually measured by sensors, and the necessary control inputs are then computed based on these data to maintain the drone's trajectory. Path tracking controllers can be utilized by agricultural drones to adhere to pre-established routes, like those generated using waypoints or GPS locations, as illustrated in Figure 7:2 The agricultural drone can track along the designated path with almost no errors, according to Figure 16, due to the fuzzy PID controllers. Figure 17 shows two-dimensional path tracking. The agricultural drone's track for is shown as a blue line along the horizontal axis and a red line along the vertical axis. In  
Figure 7:3 Velocity response of the agricultural drone at noise input. the graphs, the black and yellow dots indicate the obstacle, while the green dot shows the agricultural drone's moment. The drone's guided path is indicated by a red arrow in the curve. Agricultural drones can employ route tracking controllers to avoid obstacles like trees, buildings, and other objects in agricultural areas. The agricultural drone in this study is correctly tracked on a particular path. This drone can additionally fly in formation using route tracking controllers, which is helpful for jobs like pest control, aerial photography, and search and rescue missions. Generally, fuzzy PID controllers guide the correct path tracking for agricultural drones. Figure 7:4 Advanced 3D agricultural drone path tracking. Figure 7:5 Error of the agricultural drone with noise. All tabulated results were obtained automatically, which means that automatic PID tuning is the process of tuning controller gains based on a plant model (plant data).