Research on Robot Control Technology of Tomato Plant Lowering in Greenhouses

Abstract

Currently, tomato plant lowering is performed manually, which is both inefficient and costly. The manual process presents challenges in terms of efficiency and cost, creating a need for automated solutions in greenhouse environments. This paper addresses this issue by presenting the design and development of a tomato-plant-lowering robot utilizing machine vision and deep learning techniques. The study includes the design of an end effector optimized for plant-lowering operations based on the physical characteristics of tomato vines and roller hooks; precise positioning of roller hooks achieved through kinematic analysis and a custom dataset; integration of the RepC3 module from RT-DETR with YOLOv5s for enhanced object detection and positioning; and real-time camera feed display through an integrated application. Performance evaluation through experimental tests shows improvements in recognition accuracy, positioning precision, and operational efficiency, although the robot’s success rate in leaf removal needs further enhancement. This research provides a solid foundation for future developments in plant-lowering robots and offers practical insights and technical guidance.