In greenhouse tomato cultivation, three primary methods of flower pollination exist: insect pollination, physical pollination by vibrating flowers, and artificial pollination using hormone-based chemicals. Insect pollination, the natural method, involves insects (e.g., honeybees) vibrating flowers to collect pollen and nectar. This paper proposes an alternate approach, using small drones to search and pollinate flowers in place of bees autonomously. We report field experiments conducted using these drone technologies. The drone must locate flowers ready for pollination. We developed an artificial intelligence (AI) image classification system (AI classifier) using machine learning to identify these flowers. Equipped with an AI classifier, the drone searches for flowers through autonomous flight and positioning technology. Upon identifying a suitable flower during its search, the drone makes contact to pollinate it. Integrating AI-based flower detection, autonomous flight control for flower search, and a pollination control device allows the drone to perform pollination. This study devises these technologies, implements them in a drone, and evaluates their effectiveness through a pollination experiment.

In this study, we realized a relay network using drones and analyzed the impact of the sway angle of a drone’s attitude on communication in a windy flight environment. Drones in flight act as radio relay stations and communication among them is performed using radio equipment mounted on board. In a windy environment, a directional antenna is used for relay communication among them to avoid interference caused by the spread of radio waves in space and extend the relay distance. However, when wind occurs during flight, the flying attitude of the drone inclines, which causes the beam of the antenna to sway and the communication link to be disconnected, leading to a decrease in the transmission speed. In this study, we statistically evaluated the pitch, yaw, and roll axes of a drone through wind tunnel experiments. Furthermore, the pattern of the swing angle of the drone with respect to the wind speed was investigated using computer simulations to analyze the fluid dynamics and theoretically analyze the swing of the drone. Based on these results, the transmission speed when using a directional antenna was calculated. When the wind speed was 6.0 m/s, the pitch axis deflection angle of the drone was 13${}^{\circ }$ at maximum, and the average transmission speed decreased by 33.3 Mbps. In this study, it was found that in communication between drones due to the wind, the transmission efficiency decreased depending on the sway angle of the aircraft.

Stable pear cultivation relies on cross-pollination, which typically depends on insects or wind. However, natural pollination is often inconsistent due to environmental factors such as temperature and humidity. To ensure reliable fruit set, artificial pollination methods such as wind-powered pollen sprayers are widely used. While effective, these methods require significant labor and operational costs, highlighting the need for a more efficient alternative. To address this issue, this study aims to develop a fully automated drone-based pollination system that integrates Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs). The system is designed to perform artificial pollination while maintaining conventional pear cultivation practices. Demonstration experiments were conducted to evaluate the system’s effectiveness. Results showed that drone pollination achieved a fruit set rate comparable to conventional methods, confirming its feasibility as a labor-saving alternative. This study establishes a practical drone pollination system that eliminates the need for wind, insects, or human labor. By maintaining traditional cultivation practices while improving efficiency, this technology offers a promising solution for sustainable pear production.

Since ancient times, the anomalous occurrence of mushrooms has been observed in and around locations struck by lightning. Based on this phenomenon, various cultivation techniques using a high voltage to cultivate shiitake mushroom in bed log (wood) have been proposed. Undoubtedly, the fruiting capacity of shiitake mushrooms is significantly promoted by direct application of a high voltage to the cultivation bed logs. However, mushroom bed logs are rarely struck directly by lightning in the natural environment. Thus, it is important to devise a stimulation method that matches conditions in the natural environment. In this study, we performed experiments involving indirect lightning strikes using a new method that mimics the natural environment as closely as possible. Specifically, we used the electric discharge created by an impulse voltage generator to create lightning but did not apply it directly to the cultivation bed log. Instead, the cultivation bed logs were placed a few meters away from the lightning rod. Subsequently, the fruiting capacity of the shiitake mushrooms with and without the indirect lightning strikes was compared. The experimental results indicated that, with the indirect lightning strikes, the extent of shiitake fruiting almost doubled compared to the case without the indirect lightning strikes. Furthermore, the results confirm that an increased shiitake mushroom production is possible using lightning strikes, even if the associated high voltages are not applied directly to the bed logs.

This paper proposes a cluster-based flight path construction method for automated drone-assisted pear pollination systems in orchard environments. The approach uses RGB-D (Red-Green-Blue-Depth) sensing through an observation drone equipped with RGB and depth cameras to detect blooming pear flowers. Flower detection is performed using a YOLO (You Only Look Once)-based object detection algorithm, and three-dimensional flower positions are estimated by integrating depth information with the drone’s positional and orientation data in the east-north-up coordinate system. To enhance pollination efficiency, the method applies the OPTICS (Ordering Points To Identify the Clustering Structure) algorithm to group detected flowers based on spatial proximity that correspond to branch-level distributions. The cluster centroids then construct a collision-free flight path, with offset vectors ensuring safe navigation and appropriate nozzle orientation for effective pollen spraying. Field experiments conducted using RTK-GNSS-based flight control confirmed the accuracy and stability of generated flight trajectories. The drone hovered in front of each flower cluster and performed uniform spraying along the planned path. The method achieved a fruit set rate of 62.1%, exceeding natural pollination at 53.6% and compared to the 61.9% of manual pollination. These results demonstrate the effectiveness and practicability of the method for real-world deployment in pear orchards.