# Digital Twin-Based Vibration Monitoring of Plant Factory Transplanting Machine
## 1 Introduction
[20] demonstrated the effectiveness of a four-dimensional fusion twin model. This model incorporates multi-source heterogeneous data classification, cleaning, parsing, and real-time driving, enabling the real-time mapping of behavior, states, and actions. Numerous excellent cases have greatly promoted the development of digital twin technology [21, 22, 23, 24, 25, 26]. However, existing research mostly focuses on the innovation of digital twin frameworks and concepts, and digital twin modeling techniques are still a hot and difficult research area in the field of smart manufacturing [27]. During the transplanting process in a plant factory, mechanical vibrations that cause the gripper to shake can result in vegetable seedlings falling off and failing to be transplanted into the tray. The quality of planting operations has a significant impact on the yield of plant factories, making the physical monitoring of the transplanting system necessary. Analyzing the vibration characteristics during monitoring provides data support for the subsequent use of damping materials to reduce the vibration and noise. Therefore, this paper proposes to create a digital twin of a plant factory transplanter to visually represent the physical properties and potential laws of the factory equipment through a physical model, construct a digital twin behavior model through a standardized description method, and optimize the operational performance by overlaying feedback control functions on top of the hardware module through an information decision model. This method can achieve virtual-to-real mappingof the planting process in plant factories, providing technical references for the automated monitoring and control of agricultural production in various scenarios. In terms of vibration monitoring, [PERSON], [PERSON], and others proposed a study on predictive maintenance for CNC machine tools, which combines the DT with a sensor network to achieve early fault diagnosis for CNC machine tools. [PERSON] and others conducted research on key technologies for monitoring the manufacturing process state of automated production lines based on digital twins. They proposed a lightweight state monitoring model based on gated recurrent unit neural networks, providing a foundation for constructing digital twin models of automated production line manufacturing processes at the macro level. These studies provide a background foundation for researching vibration monitoring methods for plant transplanting machines based on digital twins. This study extensively investigates the physical entity requirements and implementation strategies of the plant factory transplant system using DT technology, as shown in Figure 1. The remaining sections of this paper are organized as follows. Section 2 presents the analysis of the application requirements for the plant factory transplant system DT, the development of application-oriented deployment plans based on axiomatic design (AD) theory, and the workflow of physical entity implementation strategies. Subsequently, in Section 3, the feasibility and effectiveness of the proposed method are validated through a comparative analysis of sensor deployment plans and vibration diagnostics for the plant factory transplant (PFT) machine. Finally, Section 4 summarizes the findings of this study.