Abstract: In the process of high-yield cultivation of greenhouse cut flower, high labour consuming and lacking of operational standardization stand out as significant issues. As an important agronomic technique for seedlings rooting, cuttage is a key link in the planting process and an important measure to increase agricultural income, accounting for 70% of the total labor cost. A carnation cuttage robot system was designed to integrate with the grasping-cuttage multifunction for the carnations' automatic cuttage. Taking carnation cuttings as the research object, according to the agronomic requirements under high-yield cultivation models and the materials characteristics of carnation cuttings, an auxiliary cuttage end-effector operation mode and a grasping-cuttage coordinated motion planning model are proposed. It realized high-efficient cuttage operation by the combination of cuttings gripping and auxiliary cuttage. A multi-objective identification and localization model was constructed based on the YOLOv5s deep learning network and the hand-eye coordinate system mapping relationship to meet the needs of target detection. Additionally, a kinematic model was built for the cuttage robot, and MD-H (modified denavit-hartenberg) rule based inverse kinematics modeling method and time optimal trajectory planning algorithm are proposed. A multi-objective cuttage points traversal simulation method was proposed to verify that the inverse kinematics modeling method based on MD-H rule can effectively reduce the joint mutation rate and solution time of inverse kinematics. In the framework of Ubuntu 18.04 and ROS-melodic, Moveit-gazebo is used to carry out the joint simulation of time optimal trajectory planning, and the validity and efficiency of time optimal trajectory planning algorithm are verified. Finally, the key technologies of the cuttage robot were integrated to develop the standardized and automated cuttage workflow. We constructed a comprehensive test bench for the cuttage robot to evaluate the localization error and machine performance. The localization average error in X and Y directions during the grasping operation were 2.33 and 2.38 mm, the cuttage operation had an average localization error in X, Y and Z directions were 2.21, 2.25 and 2.05 mm, respectively. On average, it took 14.787 s in total to operation per plant. The time of recognizing and grasping, cuttage were 6.803 and 5.104 s, accounting for 46.01% and 34.52% of the total working time respectively. During the test of the performance of cuttage robot, the average qualified rate of cuttage depth and the vertical degree of cuttage were 88% and 92% respectively. The average rate of missing cuttage and root damage were 5% and 2% respectively. The cuttage robot met the basic requirements of standardized cuttage operations. Thus, the study fills gaps in the literature on mechanized cuttage of carnation in China and provides a theoretical basis for standardized seedlings cuttage. The main factors limiting the cuttage efficiency of the robot include: 1) The greater the difference in the shape of the cuttings on the conveyor belt, the longer the detection operation time will be, thus restricting the efficiency of the grasping operation; 2) In order to ensure the safety and accuracy of the robot, it needs to complete two complex actions of grasping and cuttage, so it takes more time to complete the calculation, which affects the overall efficiency of cuttage. The irregular shape of carnation cuttings, the unstable posture and the occlusion of stems and leaves are the main reasons that affected the success rate of visual detection and led to missing cuttage. Future research should be carried out on the attitude estimation algorithm for disordered cuttings to achieve the disordered grasping of multi-objective and multi-pose cuttings. The researches on other crops are needed to further improve the performance and applicability of the robot. Therefore, it can provide more comprehensive theoretical support for the high-efficient and standardized operation of cuttage robot.