Digital twin technology digitizes the entire lifecycle of physical entities to achieve monitoring and control of their states, providing a solution for remote control and continuous operation of robots. The high-precision control of the operation chassis during the driving process is the key to ensuring the quality of robot operation. Aiming to address the issues of large error in the driving state prediction model due to changes in greenhouse environment and chassis wear, as well as the difficulty of online dynamic data collection, a digital twin based online monitoring method for the driving state of the greenhouse operation chassis was proposed. Firstly, a digital twin system of the greenhouse operation chassis geared towards the driving state was developed. It dynamically perceived the dynamic data in the driving process online and simulated the change process of the chassis driving state in real time. Then an online prediction model of the driving state of the greenhouse operation chassis was constructed by combining the temporal deviation quantification model of the chassis driving state and considering various uncertain factors during the driving process. Finally, an experimental environment for online monitoring of the chassis driving state was set up, and online monitoring experiments and driving effect verification tests were carried out. The results showed that the online prediction method proposed corresponded to lateral offset prediction accuracies of 96.32% , 95.96% , 95.69% and 96.11% for datasets M1 , M2 , M3 , M4 , respectively. The longitudinal offset prediction accuracies were 96.58% , 96.36% , 96.51% and 96.13% for datasets M1 , M2 , M3 , M4 , respectively. Compared with the BP + SVR method, the prediction accuracy of lateral displacement was increased by 3.61% , 3.26% , 3.92% , and 3.98% , respectively, and the prediction accuracy of longitudinal displacement was increased by 2.96% , 2.78% , 3.27% , and 3.06% , respectively. This proved that the online prediction method proposed can effectively correct for the bias effects caused by ground fluctuations and chassis wear and tear. The average values of the actual lateral and longitudinal deviations of the chassis during driving were reduced by 48.13% and 49.49% , respectively compared with the chassis driving method based on fixed driving parameters. This method can dynamically adjust based on the real-time driving status of the chassis. The online monitoring method for greenhouse operation chassis driving status based on digital twins had the characteristics of strong real-time and high accuracy, which can provide a basis and reference for the continuous operation method and technology of robots in facility agriculture.