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Abstract Pear pollination is performed by artificial pollination because the pollination rate through insect pollination is not stable. Pollen must be collected to secure sufficient pollen for artificial pollination. However, recently, collecting sufficient amounts of pollen in Japan has become difficult, resulting in increased imports from overseas. To solve this problem, improving the efficiency of pollen collection and strengthening the domestic supply and demand system is necessary. In this study, we proposed an Artificial Intelligence (AI)-based method to estimate the amount of pear pollen. The proposed method used a deep learning-based object detection algorithm, You Only Look Once (YOLO), to classify and detect flower shapes in five stages, from bud to flowering, and to estimate the pollen amount. In this study, the performance of the proposed method was discussed by analyzing the accuracy and error of classification for multiple flower varieties. Although this study only discussed the performance of estimating the amount of pollen collected, in the future, we aim to establish a technique for estimating the time of maximum pollen collection using the method proposed in this study.

The purpose of this study is to discuss epidemic communication for drones to share information in flight and to develop a wireless system for implementation. Various theoretical studies have been conducted on epidemic communication, but their applications are not clear, so a system that assumes practical use is developed. As the main evaluation items, we analyzed the effect of communication interference between drones on the amount of data transmission, and furthermore, proposed an optimal transmission method depending on the flight speed. In these analysis results, we designed functions to be implemented in drones, developed wireless devices, and confirmed their operation through demonstration tests using actual drones. Based on the results of this research, we succeeded in identifying issues to be addressed in order to implement the system on drones and in developing an epidemic communication system based on the results of demonstration experiments, thereby contributing to the realization of inter-drone communication in the future.

Wireless power transfer (WPT) has attracted significant attention as a method of supplying energy to low-power devices, such as Internet of Things (IoT) and sensor devices. In WPT systems, beamforming techniques based on array antenna signal processing, such as multiple-input multiple-output (MIMO) and multiple-input single-output (MISO), are mandatory to improve energy conversion efficiency. In this study, to fairly and efficiently transfer energy for multiple receivers, we consider time-division WPT (TD-WPT) in a MISO-based WPT system, where the transmitter periodically switches weight vectors for receivers by assigning a time slot to each weight vector. From two properties, beneficial interference and channel dependency in WPT, we propose a TD-WPT system with receiver grouping, where the receivers are divided into some groups, and the weight vectors and slot lengths are optimized for the groups. In the proposed method, we consider two methods for receiver grouping, distance-based grouping and channel-based grouping, where the former and the latter divide receivers according to the distances between receivers and similarities of the channel coefficients, respectively. Additionally, we extend the channel-based grouping method by considering the homogeneity of the supplied power levels. Simulation experiments in a multipath indoor environment show that the proposed method improves the minimum and average harvested energy.

Abstract Three pollination methods are commonly used in the greenhouse cultivation of tomato. These are pollination using insects, artificial pollination (by manually vibrating flowers), and plant growth regulators. Insect pollination is the preferred natural technique. We propose a new pollination method, using flower classification technology with Artificial Intelligence (AI) administered by drones or robots. To pollinate tomato flowers, drones or robots must recognize and classify flowers that are ready to be pollinated. Therefore, we created an AI image classification system using a machine learning convolutional neural network (CNN). A challenge is to successfully classify flowers while the drone or robot is constantly moving. For example, when the plant is shaking due to wind or vibration caused by the drones or robots. The AI classifier was based on an image analysis algorithm for pollination flower shape. The experiment was performed in a tomato greenhouse and aimed for an accuracy rate of at least 70% for sufficient pollination. The most suitable flower shape was confirmed by the fruiting rate. Tomato fruit with the best shape were formed by this method. Although we targeted tomatoes, the AI image classification technology is adaptable for cultivating other species for a smart agricultural future.

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.

Networks constructed in the sky are known as non-terrestrial networks (NTNs). As an example of an NTN, relay transmission using drones as radio stations enables flexible network construction in the air by performing handovers with ground stations. However, the presence of structures or obstacles in the flight path causes multipath interference; consequently, the propagation environment fluctuates significantly based on the flight. In such a communication environment, it is difficult for a drone to select an optimal ground station for a handover. Moreover, unlike a terrestrial network, the propagation environment of a flying drone is affected by structures and other factors that cause multipaths based on the flight speed and altitude, making the conditions of the propagation environment even more complex. To solve these problems, we propose handover schemes between drones and the ground that consider the multipath interference caused by obstacles. The proposed methods are used to perform handovers based on an optimal threshold of received power considering interference and avoid unnecessary handovers based on the moving speed, which makes the handover seamless. Finally, we develop a simulator that evaluates the cross layer from propagation to upper network protocols in a virtual space, including buildings, evaluate the communication quality of a drone flying in a three-dimensional space, and confirm the effectiveness of the proposed methods as well as the evaluation of the real environment.

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∘ 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.

We consider a UAV (Unmanned Aerial Vehicle) relay network whereby each UAV senses data and forwards it to dedicated ground stations by means of multi-hop relaying. In particular, we focus on a UAV relay network with a simple yet realistic linear topology for which we propose an adaptive direction control scheme to achieve a high throughput performance. In the proposed scheme, each UAV, equipped with multiple directional antennas, selects either the Decode-and-Forward (DF) straight relaying method or the orthogonal relaying method, for full-duplex data transfer, i.e., data is transmitted and received simultaneously on the same frequency, but at different antennas. The originality of our proposed method is to make each UAV rotate relatively to the position of its neighbouring UAVs, in order to optimize its antenna radiation direction, according to the selected relaying method. The key advantage of the proposed method is that signal direction can be controlled without the need of heavy adaptive signal processing as in conventional beamforming techniques. In order to clarify the decision parameters for selecting a relaying method, we first evaluate the throughput performance of the two-hop relay network under the severe interference conditions of UAV networks, and next, propose a procedure for selecting a multihop relaying method. Numerical experiments show that the proposed scheme enables to achieve a high throughput performance, with low computational costs.

Recently, the millimeter-wave band has been used in outdoor wireless communications to improve the frequency utilization efficiency. In this study, we propose a method to realize line-of-sight multiple-input multiple-output (LOS-MIMO) transmission independent of the transmitted distance using a two-dimensional (2-D) fixed antenna element arrangement, assuming millimeter-wave-band communication by small autonomous unmanned aerial vehicles. In conventional LOS-MIMO transmission with uniformly spaced antenna elements, the theoretical upper bound of the channel capacity is obtained, which decreases at a certain transmitted distance. Therefore, we focus on the fact that the propagation channel characteristics in pure LOS are geometrically determined by the transmitted distance, and consider the optimization of the arrangement of antenna elements. The element arrangement is fixed without using antenna selection from the viewpoint of system simplification. In addition, from the viewpoint of array size, we study the optimization corresponding to a 2-D element arrangement. The proposed method approximates the optimal arrangement from a vast number of 2-D element arrangements using a genetic algorithm. We evaluate the channel capacity characteristics of the proposed method by computer simulation and show that the characteristic degradation due to change in the transmitted distance in conventional LOS-MIMO transmission is improved. In addition, we evaluate a 4 × 4 MIMO transmission in an outdoor environment. The 4 × 4 MIMO propagation channel is also measured by an actual measurement equipment in the 66-GHz band and it is shown that the measured and calculated results agree with each other.

This paper addresses the problem of cochannel interference (CCI) generated in a mixed cell architecture in microcellular systems. In this type of microcellular systems in which both microcells and macrocells coexist in the same geographical urban area, the base station antennas mounted on the rooftops of buildings to cover wide circular radio zones suffer severe CCI from the surrounding low base stations. A dielectric-loaded slotted-cylinder antenna (DSCA) is applied to horizontally polarized omnidirectional array antennas in a height-diversity configuration with the high gain of 8 dBi, which is comparable to that of a collinear antenna, to reduce the CCI. The measurements conducted in a suburban area clarify the reduction in the CCI for three techniques. The beam-tilt technique reduces the CCI level by approximately 10 dB for both collinear antennas and the DSCA array antennas. The use of horizontal polarization reduces the CCI level by approximately 13 dB for the DSCA array antennas with and without beam tilt. The combination of the beam tilt and horizontal polarization or the DSCA array antennas with beam tilt significantly reduces the CCI level by approximately 23 dB.