## 4 Results and Discussion
On the other hand, the agricultural drone orientation takes longer to get used to. When comparing the orientation of an agricultural drone packed with a fuzzy PID and a PID controller, the former takes less time to settle, more quickly reaches overshoot, is smoothest after reaching the settlement time, and does not have a global minimum. On the other hand, the latter takes more time to settle, more slowly reaches overshoot, chatters after reaching the settlement time, and has a global minimum. Both the fuzzy PID and PID controllers' performance with regard to the agricultural drone system can be seen in Figure 9. When compared to the farm drone controlled using a PID controller, the drone controlled using a fuzzy PID controller performed better at a given velocity with the same input. The obtained results suggest that the agricultural velocity with the hybrid fuzzy PID controller takes less time to settle, achieves overshoot faster, and has a smoother trajectory after reaching the settlement time. In contrast, the agricultural velocity using a PID controller has two global maximum points and is cyclically stable over time. Figure 8: Orientation controller of agricultural drone. Figure 9: Velocity controller. The errors resulting from the drone performance in agriculture are shown in Figure 10. These errors were acquired by subtracting the PID controller from the agricultural drone system with a fuzzy PID controller. Positional errors rose to a predetermined value before stabilizing at the lowest possible value. While there appears to be less noise throughout the orientation portion of the performance than during the velocity portion, the position appears to be dynamic throughout. Because the hybrid and individual controllers operate similarly, the position case becomes smooth. In contrast, the error is not smoother prior to the orientation performance, since the hybrid and individual controller performances deviated. The test results of the fuzzy, fuzzy PID, and PID controller efforts at the noise input are shown in Figure 11. The maintenance effort for a fuzzy logic controller alone (fuzzy controller effort) is usually less than that of a conventional control system. This is due to the fact that fuzzy logic controllers are less susceptible to changes in the system they are controlling and are more resilient. Because it shares some advantages with fuzzy logic controllers, such as being more efficient than traditional control systems, and with PID controllers, such as eliminating steady-state errors and minimizing overshoot and oscillations, the hybrid controller's effort becomes smooth throughout the performance. Only the PID controller revealed some system-wide noise. This is due to the possibility that in complex systems with highly nonlinear dynamics or large time delays, controllers may find it difficult to deliver optimal control performance. The agricultural drone continued to operate as intended, with the fuzzy logic algorithm adjusting the issues based on feedback. Figure 11: Controller effort with noise. Figure 10: Errors response. The input given was noise, as seen in Figure 12. The fuzzy PID (hybrid) and PID controllers' performance with regard to the agricultural drone was obtained based on the input given. The performance of the position remained constant throughout, since there was no difference in the strength of the controller response to an input or output signal deviation. The controller gain for an agricultural drone in a control loop is the amount of action a controller will take at a specific point (position) below or above the setpoint. Figure 13 depicts the varied performance for the orientation of the farm drone. The agricultural drone's PID controller does not make the system's orientation any smoother. PID controllers may cause instability or subpar control performance because they are susceptible to noise in the measurement or control signals. PID controller parameter tuning can be difficult, particularly for systems with complicated dynamics.