Pesticides are essential for removing plant pests and sustaining good yields on agricultural land. Excessive use has detrimental repercussions, such as the depletion of soil fertility and the proliferation of immune insect species, such as Nilaparvata lunges and Nezara viridula. Unmanned aerial vehicle (UAV) variable-rate spraying offers a precise and adaptable alternative strategy for overcoming these challenges. This study explores research trends in the application of semi-automatic approaches and land-specific platforms for precision spraying. The employment of an autonomous control system, together with a selection of hardware such as microcontrollers, sensors, pumps, and nozzles, yields the performance necessary to accomplish spraying precision, UAV performance efficacy, and flexibility in meeting plant pesticide requirements. This paper discusses the implications of ongoing and developing research. The comparison of hardware, control system approaches, and data acquisition from the parameters of each study is presented to facilitate future research. Future research is incentivized to continue the precision performance of the variable rate development by combining it with cropland mapping to determine the need for pesticides, although strict limits on the amount of spraying make it difficult to achieve the same, even though the quality is very beneficial.

Pest control is essential for increasing agricultural production. Agricultural drones with spraying systems for pest control have generated great interest among farmers. However, spraying systems installed on unmanned aerial vehicles, like any other sprayer, can cause damage to the environment due to drift of the agent. Air induction (AI) nozzles are known to produce less drift (e.g., larger spray drops) than other nozzles, but there is a lack of research analyzing their effectiveness in combination with drones. In this study, AI and flat fan nozzles were installed on drones to evaluate their spray and pest control performance. Aerial spraying was conducted on rice and soybeans to measure the coverage and penetration ratio and analyze the crop production as well as the crop damage due to pests and diseases. The drone flight was conducted at an altitude of 3 m and a velocity of 2 m/s. Spray droplets were collected using water-sensitive paper at two heights above the soil surface. The experiments showed that the crop coverage with the AI nozzle was 130% higher than that with the flat fan nozzle. The drift reduction of AI nozzles increased the coverage of spray droplets. But the difference in the penetration ratios, which is the ratio of agents to be delivered inside the crop, was not significant between the nozzles. Also, there was no significant difference in crop yield and pest control efficacy. Consequently, the performance of the AI nozzle did not show differences from that of the XR nozzle, except for coverage. However, the AI nozzle raised less drift, so it should be considered for use in aerial control.

Honeybees pollinate over 75% of the total food resources produced annually, and they produce valuable hive products, such as bee pollen, propolis, and royal jelly. However, species such as the Asian hornet (Vespa velutina) feed on more than 85% of honeybees, causing a decline in their population and considerable damage to beekeepers in Korea. To prevent damage to honeybees, a portable real-time monitoring system was developed that detects V. velutina individuals and notifies users of their presence. This system was designed with a focus on portability and ease of installation, as V. velutina can be found in various areas of apiary sites. To detect V. velutina, an improved convolutional neural network YOLOv5s was trained on 1960 high-resolution ($3840\times 2160$) image data. At the confidence threshold of ≥0.600 and intersection over the union of ≥0.500, the performance of the system in terms of detection accuracy, precision, recall, F1 score, and mean average precision was high. A distance-based performance comparison showed that the system was able to detect V. velutina individuals while monitoring three beehives. During a field test of monitoring three beehives, the system could detect 83.3% of V. velutina during their hunting activities and send alarms to registered mobile application users.