## 2 Literature Review
### Path Planning in Agricultural Applications  
It is very important that autonomous robots used in smart agriculture perform their duties efficiently and that resources are used efficiently. In this regard, a vital issue is that these robots do their tasks with the most optimum mechanism. Therefore, it is necessary to focus on the NP-hard type of 3D path planning problem. A general classification of 3D path planning consists of four types, as shown in Figure 1. These types are sampling-based algorithms [40], node-based algorithms [41], mathematical-based algorithms [42], and nature-based algorithms [43]. The methods in the first three categories suffer from high time complexity and local minima trap, especially when mobile robots face multiple constraints when planning a path. Metaheuristic algorithms, a set of nature-inspired algorithms, are the fourth category in this taxonomy that imitate natural, biological, interactive behaviors or physical events [44; 45]. These methods try to find an almost optimal path by eliminating the process of creating complex environment models based on stochastic approaches. The stochastic approaches can be efficient and fast in solving large and complex optimization problems, especially in non-differentiable, multi-objective, and multimodal problems [20; 46]. Finding the best shortest path entails some problems such as the existence of many possible obstacles in its route. In addition, this path should be smooth without sharp turns and must consider movement restrictions. These problems may be even more cumbersome when considering large land areas and similar agricultural environments. Solution techniques in path planning algorithms for mobile autonomous mobile robots may include a visibility graph [48], probable road maps [49], and random exploring algorithms [50]. However, judging from the results of numerous studies in the literature, metaheuristic methods may be better overall [51; 52; 53]. Metaheuristic methods try to find an almost optimal path by eliminating the process of creating complex environment models based on stochastic approaches. These methods are among the most appropriate approaches to solve unifying and nonlinear global optimization problems [54]. Worth mentioning here is the No-Free-Lunch (NFL) [55] theorem. It asserts that there is no specific metaheuristic algorithm that provides the best solution for every optimization problem. This means that if one algorithm can solve a kind of problem effectively, then it may not be effective to solve another kind of problem. As such, there is a considerable demand to develop new metaheuristic algorithms that can be used in various problems. As previously stated, the path planning problem has become popular in recent years and the metaheuristic algorithms can be the most appropriate solution for it, but in the literature, there are not many works that study agricultural lands for various purposes. Many agricultural studies in the literature have focused on issues such as the farmer's income from harvest, the variety of land use, the type and amount of employment, labor productivity, biodiversity indices based on landscape ecological measures, and soil erosion [56; 57; 58]. In the literature, although there are some studies on path planning in agricultures [59; 60], they have generally not focused either on 3D path planning or on the problem of having many obstacles in the real environment farmlands and how to detect them. In [61], the authors addressed the coverage path problem in a particular region with many known obstacles for mobile robots in agriculture. The study proposed a practical method, considering the geometry properties and obstacles of the area. It used an obstacle avoidance mechanism to find a coverage path for agricultural drones. However, optimal pathfinding and its usability in a 3D space were not taken into account. Additionally, the complexity time and space of their proposed method are not efficient in comparison with metaheuristic-based algorithms.