For agricultural applications, regularized smart-farming solutions are being considered, including the use of unmanned aerial vehicles (UAVs). The UAVs combine information and communication technologies, robots, artificial intelligence, big data, and the Internet of Things. The agricultural UAVs are highly capable, and their use has expanded across all areas of agriculture, including pesticide and fertilizer spraying, seed sowing, and growth assessment and mapping. Accordingly, the market for agricultural UAVs is expected to continue growing with the related technologies. In this study, we consider the latest trends and applications of leading technologies related to agricultural UAVs, control technologies, equipment, and development. We discuss the use of UAVs in real agricultural environments. Furthermore, the future development of the agricultural UAVs and their challenges are presented.

The introduction of multiple unmanned aerial vehicle (UAV) systems into agriculture causes an increase in work efficiency and a decrease in operator fatigue. However, systems that are commonly used in agriculture perform tasks using a single UAV with a centralized controller. In this study, we develop a multi-UAV system for agriculture using the distributed swarm control algorithm and evaluate the performance of the system. The performance of the proposed agricultural multi-UAV system is quantitatively evaluated and analyzed through four experimental cases: single UAV with autonomous control, multiple UAVs with autonomous control, single UAV with remote control, and multiple UAVs with remote control. Moreover, the performance of each system was analyzed through seven performance metrics: total time, setup time, flight time, battery consumption, inaccuracy of land, haptic control effort, and coverage ratio. Experimental results indicate that the performance of the multi-UAV system is significantly superior to the single-UAV system.

Fruit and vegetable harvesting robots have been widely studied and developed in recent years. However, despite extensive research commercial tomato harvesting robots still remain a challenge. In this paper, we propose an efficient tomato harvesting robot that combines the principle of 3D perception, Manipulation, and an End-effector. For this robot, tomatoes are detected based on deep learning, after which 3D coordinates of the target crop are extracted and motion control of the manipulator based on 3D coordination. In addition, a suction pad featuring the kirigami pattern, which is a part of the suction gripper, was developed to grip individual tomatoes in clusters. A scissor-shaped cutting module with an assist unit, which is used to overcome structural limitations and implement effective cutting, was also desinged and tested. The proposed tomato harvesting robot was validated and evaluated on a laboratory testbed basd on the performance of each component. Therefore, in this study, we propose and verify a new robot design for the effective harvesting of tomatoes.

Path planning is crucial for several applications, including those in industrial facilities, network traffic, computer games, and agriculture. Enabling automated path-planning methods in smart farms is essential to the future development of agricultural technology. Path planning is divided into global and local planners. Global planners are divided into different types and use well-known grid-based and sampling-based algorithms. In this paper, we propose an algorithm suitable for smart farms in combination with simultaneous localization and mapping (SLAM) technology. The characteristics of the grid-based Dijkstra algorithm, the grid-based A* algorithm, the sampling-based rapidly exploring random tree (RRT) algorithm, and the sampling-based RRT* algorithm are discussed, and an algorithm suitable for smart farms is investigated through field tests. We hypothesized path planning for an agricultural harvesting robot, a spraying robot, and an agricultural transport robot, and conducted experiments in environments with static and dynamic obstacles. In addition, the set parameters are validated experimentally. The Shapiro–Wilk test is used to confirm the shape of the normal distribution, and the analysis of variance (ANOVA) and Kruskal–Wallis test are performed to confirm the significance of the experimental results. Smart farms aim to minimize crop damage; thus, it is vital to reach the goal point accurately rather than quickly. Based on the results, we determined that the A* algorithm is suitable for smart farms. The results also open the possibility of reaching the correct destination in the shortest time when working in smart farms.

Cooperation among heterogeneous agricultural robots in an agricultural environment guarantees the advantages of effectiveness and scalability. However, traditional control theories for coordination among multiple heterogeneous robots lack a systematic modeling method and a control strategy under unstructured and uncertain environments. To handle these limitations, a novel approach based on the Ramadge–Wonham theory and discrete event system is proposed in this paper. Specifications and a supervisory controller based on discrete event models were defined from an agricultural perspective considering cooperation among heterogeneous agricultural field robots. Discrete event systems were modeled through the automata theory and the behavior of heterogeneous field robots satisfied the designed specifications. The resulting supervisor ensures that the control objectives of formation control, obstacle avoidance, movements, and path following are satisfied. The approach and architecture proposed in this study were validated using a physics-based simulator and field experiments.