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.

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.

Smart agriculture has the potential to solve labor shortages and improve production efficiency and prices at the time of shipment. Predicting tomato yields during the cultivation period is crucial for planning shipment volumes and costs in advance. We propose technology that utilizes an AI camera to enable producers to predict yields more accurately, and we verify the effectiveness of the developed system through experimental validation. Specifically, an AI-recognition camera was developed, utilizing You Only Look Once (YOLO) to detect individual tomatoes. The detected tomatoes are analyzed for size using point cloud data. Moreover, the AI-recognition camera performs to classify ripeness based on hue. This technology can achieve accurate ripeness classification without being dependent on the brightness of the greenhouse. To evaluate this AI classification camera, the predicted yield obtained from the camera was compared with the actual harvested yield in the field. The analysis showed an error rate of 6.85%, demonstrating sufficient accuracy for practical implementation. By introducing this system, efficient yield prediction can be achieved, leading to reduced labor costs, stable tomato supply, improved quality, and optimized market distribution. As a result, it is expected to contribute to the benefits of both shippers and consumers.

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.