Clip-assisted flower detection and wind-compensated precision liquid pollination robot for kiwifruit orchards

Abstract

• Wind-compensated spray system enhances robotic kiwifruit pollination efficiency. • CLIP-based flower detection reduces dataset labor and enables real-time recognition. • Modular Python control framework provides scalable and portable robot task management. • Achieved 85 % fruit set using only 200 g/ha of pollen under field conditions. Pollination in kiwifruit orchards requires both precision and timeliness, and the widely adopted pergola training system provides favorable conditions for automation. In this study, we developed a precision liquid pollination robot for kiwifruit and systematically evaluated its field performance. The robot integrates a liquid spray control system with wind compensation, enabling accurate pollen delivery and significantly reducing pollen consumption. To overcome the bottleneck of labor-intensive data annotation, we proposed a CLIP-based automatic labeling approach to construct a kiwifruit flower detection dataset, which was then used to train a lightweight YOLO model for real-time flower recognition. A modular task control framework, developed entirely in Python, was implemented to coordinate localized spray operations and extend the effective working range of the robotic arm. Field trials conducted in Shaanxi, China, during 2024–2025 demonstrated that the robot achieved a fruit set rate above 85 %, while consuming only 200 g/ha of pollen, which is substantially lower than previous robotic systems, and operating at a speed of 35–40 s/m 2 . To ensure reproducibility, the developed framework and code have been released as open source on GitHub, and datasets have been archived on Google Drive for long-term global access. This research is the first to apply CLIP-assisted flower detection in robotic pollination, and the results confirm its potential for reducing data preparation costs, improving pollination efficiency, and providing a scalable, open robotic platform for intelligent orchard management.