## 2 Literature Review  
As stated in our previous work [8], smart agriculture (precision agriculture) is an approach to farming that uses technology to improve efficiency and productivity. It involves the use of sensors, drones, and other devices to collect data on soil conditions, crop health, and weather patterns. These data are then used to make informed decisions about planting, irrigation, and fertilization. However, the research findings in [9; 10; 11; 12; 13] indicate the presence of multiple factors that must be considered when incorporating PID controllers into drone systems for agricultural applications. For instance PID controllers are designed based on a fixed set of parameters, which may not be optimal for all operating conditions. Agricultural drones operate in diverse environments, including varying weather conditions, payload changes, and flight dynamics, and PID controllers may struggle to adapt to these changing conditions, leading to reduced control performance. PID controllers also rely on sensor feedback for their control actions. In agricultural environments, sensors can be susceptible to noise and disturbances caused by factors such as wind, vibrations, and electromagnetic interference. This noise can affect the accuracy and stability of the PID controller's output, leading to erratic drone behavior. PID controllers are not inherently robust to external disturbances and uncertainties. In agricultural operations, drones may encounter unexpected disturbances such as sudden wind gusts, turbulence, or collisions with obstacles. PID controllers may not be able to handle these disturbances effectively,potentially compromising the safety and stability of the drone. PID controllers are typically designed to optimize a single control objective, such as altitude hold or position tracking. However, agricultural drones often require the simultaneous control of multiple objectives, such as the altitude, attitude, and position. PID controllers may not be well suited to handling these multi-objective control scenarios effectively. PID controllers are relatively simple control algorithms, but they can become computationally intensive when used in complex agricultural drone systems. This can be a concern for drones with limited onboard processing power and resources, especially when multiple PID controllers are used for different control tasks [14; 15; 16]. The goal of smart farming as a management concept is to give the agricultural sector the tools it needs to use cutting-edge technology [17], such as big data, the cloud, smart mechatronics systems, and the Internet of Things (IoT), for tracking, monitoring, automating, and analyzing processes. Drones, robots, and other components of smart agriculture use electronic devices to communicate with one another in the hyper-digital world [18]. Modern technologies such as virtual reality (VR), machine learning (ML), artificial intelligence (AI), the Internet of Things, and big data are encouraging many farmers to replace their current equipment with digital devices. There has been a change from hand tools and horse-drawn plows in traditional farming to high-tech machinery, chemical fertilizers, sensors, and drones in modern agriculture. Farmers now have more control over raising cattle and cultivating crops through advanced technological devices in agricultural systems using smart mechatronics [19]. For example, automated irrigation systems and sensors for light, humidity, temperature, soil moisture, and other parameters in smart farming have allowed farmers to increase their output while decreasing waste. Robotics, GPS, big data, sensors, and actuators are integral components of smart farming systems, providing benefits such as lower operating costs, higher agricultural yields, better crop output, and reduced labor requirements. Previously, farmers had to walk around their farms to check their crops, but the use of drones now gives them deeper insights into their crops by gathering multispectral, thermal, and visual imagery while in flight.