## 4 Results and Discussion  
Figure 6 illustrates the fuzzy logic controller's effort in agricultural drones, which involves designing, implementing, and maintaining a controller. Fuzzy PID controllers use fuzzy logic to generate decisions, which are then inputted into a PID controller. The size, complexity, inputs, outputs, and performance of the system affect the design effort. The fuzzy logic controller's effort allows for the representation and manipulation of imprecise or uncertain information, allowing for a more nuanced representation of the system. It uses a PID controller in agricultural drone systems to modify the output based on discrepancies between the intended and actual output. The fuzzy logic PID controller can be used for regulation and optimization, with hybrid controllers being the most stable and error-free option. Hybrid controllers are stable over time and have fewer errors than individual PID controllers. The magnitude of the horizontal axis for all controller efforts was the same, but the magnitude of the vertical axis was different. The reason for this is that the force required by the PID controller to stabilize the UAV system is much smaller than the force required by the fuzzy PID controller. Since fuzzy PID controllers used huge energy to push the UAV system, it suddenly returned to its original position. Although the amount of force required by the PID controller was small, it was unable to return the UAV system to its initial position. Fuzzy PID controllers have improved accuracy and efficiency, especially in systems with non-linear dynamics. The reduced overshoot and settling time, resulting in a faster and more stable system response and enhanced robustness to parameter variations and disturbances, make the system more resilient to changes in operating conditions. The force required by a fuzzy controller is less compared to that required by a hybrid controller and more than that required by a PID controller. Because a fuzzy logic controller is compared to a fuzzy PID controller, fuzzy logic controllers can be more complex to design and tune. Fuzzy logic controllers typically require more expertise and effort to  
determine the appropriate fuzzy sets, membership functions, rules, and defuzzification methods. On the other hand, fuzzy PID controllers combine the advantages of classical PID controllers with the flexibility of fuzzy logic, offering a more structured and familiar framework for control system design and tuning. Additionally, fuzzy PID controllers can provide better performance in certain applications by incorporating the benefits of both fuzzy logic and PID control. All of the results were plotted until the final time frame, but due to PID controllers, the horizon became wide. As the optimal gain approached the exact controller gains, the horizon became wider. Any gains around the optimal controller are also applied to the UAV system. Figure 7 illustrates the nonlinear wave function that is used as the input for the agricultural drone. While time is displayed in milliseconds, the vertical axis is measured in force (N). Based on the input given, it was found that the agricultural drone's location was aligned the same using both fuzzy PID and PID controllers. There is consistency among them all. This suggests that the hybrid farm drone (fuzzy PID) controller performs similarly to the PID controller in terms of effort position. Figure 6: Comparison of controller effort. Figure 7: Position response for agricultural drones. Conversely, given the identical input, there is a divergence in the case of the agricultural drone's orientation (Figure 8). More work is completed with hybrid (fuzzy PID) controllers than with standalone PID controllers. This is because the agricultural drone system has elements of both the PID controller (which lowers error) and the fuzzy logic controller (which is more resilient to noise and disturbances). Because of this, the agricultural drone's orientation is established with the least amount of time needed.