Development of Pear Pollination System Using Autonomous Drones
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.
Authors
- Takefumi Hiraguri (ORCID: https://orcid.org/0000-0002-5923-136X)
- Keita Endo
- Hiroyuki Shimizu (ORCID: https://orcid.org/0000-0002-4938-1831)
- Akane Shibasaki
- Tomohito Shimada (ORCID: https://orcid.org/0009-0007-0923-0784)
- Kazuhisa Miyoshi
- Tomotaka Kimura (ORCID: https://orcid.org/0000-0003-0677-9767)
- Kunihiko Hattori
- S. Okubo
- Yoshihiro Takemura
Institutions
- Doshisha University (JP)
- Saitama Prefectural Industrial Technology Center (JP)
- Tottori University (JP)
- Nippon Institute of Technology (JP)
Publication Details
- Journal
- AgriEngineering
- Published
- 2025-03-05
- DOI
- https://doi.org/10.3390/agriengineering7030068
- Citations
- 8
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 14.00
Funders
- Bio-oriented Technology Research Advancement Institution