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.

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.

Field robot systems have recently been applied in a wide range of research fields. Further automation, development, and activation of such systems require cooperation among heterogeneous robots. Classical control theory is inefficient in managing large-scale complex dynamic systems. Therefore, a discrete-event system based on the supervisory control theory must be introduced to overcome this limitation. In this article, we propose a hybrid system-based hierarchical control architecture using a supervisory control-based high-level controller and a traditional control-based low-level controller. The hybrid system and its dynamics are modeled through a formal method, called hybrid automata, and the behavior specifications are designed to express the control objectives for cooperation. In addition, modular supervisors that are more scalable and maintainable than a centralized supervisory controller were synthesized. The proposed hybrid system and hierarchical control architecture were implemented, validated, and evaluated for performance through a physics-based simulator and field tests. The experimental results confirmed that the robot team satisfied the given specifications and presented systematic results, validating the efficiency of the proposed control architecture.

The tracking of flying insects is one of the main challenges in the field of ecosystem and biodiversity protection. In the case of invasive insects such as Asian hornets, extensive considerations must be made in estimating the habitat because of the limitations of available sensors and environmental uncertainty. In this study, we propose an approach for localizing and autonomously tracking radio-tagged flying insects and developing an unmanned aerial vehicle (UAV)-based robotic system. The extended Kalman filter is applied to the received signal strength emitted from a radio telemetry transmitter to estimate the insect’s position while reducing the measurement error and noise. The proposed autonomous tracking strategy involves a method in which the UAV rotates around one point to measure the signal strength and control its position in relation to the direction of the strongest signal. We also designed a system architecture that includes a tracking sensor system and a UAV system for radio-tagged flying insects. The effectiveness of the proposed system for estimating and autonomously tracking the target position was evaluated via numerical dynamics simulation. Furthermore, feasibility tests with unmanned ground vehicle replacements for insects and UAVs were performed to validate the proposed approach. Finally, to verify the UAV system, field tests were conducted to track a fixed radio-tagged flying target on a vast flatland. This study is unique because we propose and validate a novel mobile robot-based tracking system to localize radio tags of flying insects.