## 2 Related Work  
Research on drone and UAV technologies and their potential applications in the field of agriculture has started to be heavily explored in recent times [17; 18; 23; 24; 25]. Aerial vehicles are used to monitor the well-being of crops in fields, spraying of pesticides and various medicines to ensure the safety of the produced, etc. All these are being assisted by various technological features including machine learning, deep learning, and artificial intelligence [18; 26; 27]. Trajectory planning of UAVs has attracted the attention of researchers in many ways, both in single and multiple UAV systems, because with a selection of the most appropriate route a lot of energy can be saved [19; 20; 28; 29; 30; 31]. Therefore, to make the process of real monitoring of cultivation fields very efficient, the cost has to be optimized and hence proper trajectory planning is extremely necessary for the operations of UAV systems. The concept of energy efficiency is implemented from perspectives:  
1. Positions of changing stations [32; 33; 34]. 2. Energy-optimized execution of tasks requested by the sites [35; 36; 37; 38; 39; 40; 41; 42; 43]. The only advantage of trajectory planning based on the positions of changing stations is that energy efficiency is obtained only when UAVs are exhausted. On the other hand, task execution is a much more frequent phenomenon and if trajectories are planned based on task execution requests raised by different sites, then at each step some useful energy may be conserved. Therefore, trajectory planning depending upon task execution requests generates much better results (category (ii) mentioned above in terms of energy preservation compared to those in category (i) written above). Additionally, there is another point to be noted: if UAVs are exhausted less frequently due to the energy-efficient execution of tasks, then the need for trajectory planning based on the positions of the changing stations is also reduced. Category (ii) can be further classified into three subcategories based on specialized techniques that are applied to plan flight paths of UAVs:  
1. Ant-colony-based optimization [29; 40; 41]. 2. Mixed-integer linear programming-based techniques [42; 43]. 3. Shortest-path-based planning [20; 44]. An ant-colony optimization or ACO-based heuristic framework 75 concentrates on the optimal deployment of GCS for minimization of recharge cost of drones as well as UAVs. Mixed-integer linear programming (MILP) model-based trajectory planning is proposed in [42; 43]. Here, sensor nodes are themselves embedded with the UAV capacity (namely SN-UAV) and in their context, a flat topology routing protocol is proposed for energy optimization. However, if the agricultural land is very large in size and a lot of sensor nodes have to be deployed at various portions of it, then embedding UAV capacity in each one of those will be difficult as well as expensive. [PERSON]'s shortest-path-based algorithm is proposed where two-dimensional path planning is performed to avoid obstacles in the environment. Here, the weight of each edge is the inverse of the distance between the pair of sites, and the path with the highest weight is chosen by the UAV. This algorithm has many disadvantages. It does not consider anything other than the distance between the two sites. The UAV under this algorithm may traverse through all the regions which are unnecessary and there a huge amount of energy is wasted in spite of following the shortest paths for traversal. Another possible categorization is:  
1. UAV systems without facility of task delegation [34; 35; 36; 37; 38; 39; 40; 41; 42; 43]. 2. UAV systems with facility of task delegation [33; 44]. Task delegation contributes to overall system efficiency both in terms of energy and delay. Whenever a UAV feels exhausted and a nearby UAV is idle, the first UAV delegates some task to the second one.