# Digital Twin-Based Vibration Monitoring of Plant Factory Transplanting Machine
## 1 Introduction  
As a key piece of technical equipment in plant factories, the vegetable transplanting machine directly affects the quality and survival rate of seedlings, which in turn affects the overall yield and economic benefits [1; 2]. During the operation of high-speed transplanting robots, vibrations can occur, and mechanical vibrations can cause seedlings to slip off and fail to be properly planted on the planting ray. It is necessary to explore the impact of such mechanical vibrations on the transplanting process. Therefore, it is important to design a monitoring system that can be adapted to the plant factory environment and present the working status of transplanters in real time [2; 3]. The newest and most advanced technologies are the intelligent detection technologies that integrate virtual reality andphysical sensing networks, which offer new ideas for transplant monitoring. The digital twin is a strong driving force for this development [4, 5]. Since its proposal in 2003, the concept of the digital twin (DT) has gained significant interest from the academia and industry. It has found application primarily in the aerospace sector, addressing challenges related to equipment maintenance and failure prediction [6]. NASA has utilized digital twin technology to predict and prevent complex system failures in flight systems, while the U.S. Air Force Research Laboratory has developed a virtual computational model to forecast aircraft structural lives by considering structural deviation and temperature in a virtual space. Several studies have supported these developments [7, 8, 9, 10, 11]. Siemens, GE, RWTH Aachen University, and Pennsylvania State University, among others, have conducted separate research on the system and application of digital twin technology. Domestic ICT institutes, such as Sadie and Ansys Asia Pacific, have also made significant contributions to the development of the digital twin through white papers and exploration of its concept, technology, standardization, and practical applications [12, 13, 14, 15, 16]. [PERSON] et al. thoroughly explained the concept of the digital twin workshop under the digital twin framework. They examined the fault prediction and health management issues of wind turbines, leading to improvements in the digital twin structure. They introduced a novel five-dimensional digital twin model architecture comprising a model for the physical entity, a model for the virtual device, a service model, a digital twin data model, and an association model. The composition of each model is visually represented through an equation. In terms of replicating reality in a productive manner, [PERSON], [PERSON] et al. utilized a simplified model to meet the time-dependent requirements of the digital twin for aircraft structures. Regarding functional mapping, [PERSON], [PERSON] et al. also explored this aspect. In their study, [PERSON] [17] used the concept of a digital twin to illustrate the modular capabilities of smart objects. This approach allows physical objects to effectively map their functions in the realm of information. Concerning data flow, [PERSON] and colleagues [18] focused on the essential components of the manufacturing process at the factory. They explained the data collection of the tangible layer of the virtual replica and the real-time depiction of the manufacturing operation procedure using the unified structure of the industrial norm OPC (OLE for process control). The digital twin system emphasizes establishing a real-time connection between the physical and virtual layers, utilizing data as the medium. [PERSON] [19] examined the relationship between physical data and the information model in the context of a stamping production line. The study employed an object-oriented approach to develop a physical data model and analyzed the motion behavior and logic of the production line to construct a digital information model. In a separate study on the intelligent control problem of microassembly lines, [PERSON] et al.