## 2 Literature Review
These data can be used for a variety of purposes, including drainage planning, weed pressure mapping, wheat nitrogen content assessment, plant height measurement, plant counting, yield projection, and plant health indicators. This allows farmers to concentrate on the areas of their fields that need improvement [20; 21; 22]. However, these advantages can only be obtained if the system is controlled appropriately in order to optimize its performance. The use of AI, big data, and Internet of Things is becoming standard in modern agriculture. Other trends include boosting connectivity between users and components, embracing digital transformation among producers and consumers, and integrating sophisticated machinery and technology. Autonomy and intelligence are combined in smart agriculture to provide actionable insights that maximize productivity, sustainability, quality, and efficiency. Using wireless technology and sensors, stable drones can gather agricultural data in the field, including information on temperature, humidity, crop health, water supply, agricultural equipment functioning, soil conditions, and crop and soil nutrition. The drones send this information to the farm management system and to wheeled mobile robots via Things-Speak (Internet of Things, big data, satellite communication) [23; 24; 25]. The justifications for using fuzzy PID controllers in the current work over the different combined controller families (BELBIC PID [26], fractional order PID [27], sigmoid-PID [28], MPC-PID, LQR-PID, LQG-PID [29], and many more) include improved performance in systems with complex dynamics, better adaptability to changing environments, reduced sensitivity to parameter variations, and enhanced robustness against disturbances. Additionally, fuzzy logic allows for a more intuitive tuning process compared to traditional PID controllers. The combination of these controllers falls under the category of the PID controller family; nevertheless, they have disadvantages in relation to over-UAV state performance. For example, BELBIC PID controllers can be more sensitive to noise and measurement errors. They are more complex to design and implement than traditional PID controllers. They may not be as effective in controlling systems with large time delays. Meanwhile, fractional-order PID controllers have increased complexity in tuning due to additional parameters, potential instability if not properly designed, and limited availability of established design methods compared to traditional PID controllers. Sigmoid-PID controllers have complexity and potential challenges in tuning due to the non-linear nature of the sigmoid function. This can make them more difficult to optimize and finetune compared to traditional PID controllers [30; 31]. MPC-PID controllers require significant computational resources, which can be a limitation for real-time applications. For instance, LQG-PID controllers are more complex to design and implement compared to traditional PID controllers. LQG-PID controllers require more computational resources, which can be a limitation for em-bedded systems or real-time applications. LQG-PID controllers are not suitable for all types of systems, especially those with significant nonlinearities or time-varying dynamics [29]. Mathematical Modelling Approach and Quadcopter Controller Design for Smart Agriculture Applications