The objective of this work is to apply cutting-edge digital techniques to address several identified essential problems, from which farmers, farming, and farms have suffered for centuries. It has been found that the participants in the metaverse-related agricultural applications have been designed to be users rather than residents. There is another critical setback for the metaverse to be a fusion cyber-physical space, in which the cyber space is subject to different values principles from the physical space. A trigram metaverse of Cyber-Farm is proposed to be constructed on a unified trigram space through the fusion of cyber, physical, and values spaces. As a parallel and superstructure to the cyber and physical spaces, the values space enables the cyber space and physical space to follow the same values principles through its autonomic, values-driven, and crowd-dispatching governance system. Unlike in the existing metaverse-related agricultural applications, the Cyber-Farm participants are the subjects/residents rather than the users of a Cyber-Farm. The agricultural elements are coming into being and evolving in the interlinked and fusion trigram space. The basic production means, production relations, and superstructure of the trigram metaverse have been discussed. Both the connotations and scopes of farm, farmer, and farming have been redefined in the trigram metaverse of Cyber-Farm. The intentions, scenarios, principles, and businesses of the Cyber-Farm have been restructured. Basically, the Cyber-Farm can address the identified essential problems with today’s agriculture, while a grand vision is to bring about farm-featured Utopias parallel to human communities.

Obtaining a high-precision risk warning service, which can improve trading efficiency and legality, by reducing sampling proportion and customs clearance time dramatically is critical for cross-border trades. However, existing anti-fraudulent services are weak either in the precision or the mining capacity of discovering hidden risks. Among the reasons are incomplete data, untrustworthy resources, and old analysis models. On the basis of these observations, this article makes a combined technical solution for a risk warning service to address data resource, integrity, and mining capacity issues. The provided risk warning service is featured with a correlation analysis approach, which is advanced and efficient at addressing multisource and heterogeneous data to identify deep-seated risks with cross-border products, such as fake documents, price concealment, epidemic events, and ingredient pollution. To reveal the hidden correlation risks in cross-border clearance, a set of correlation-oriented data models and multi-attribute, multi-object, and multi-level methods are developed. The involved data sources and objects can be collected from inside businesses and public resources. Data are further structured to depict the whole portrait of a trade. The correlation analysis approach proves to be feasible and efficient in processing multisource and heterogeneous data to discover deep-seated risks with cross-border products. The risk warning service and the used correlation analysis approach have been studied and developed on the basis of a pilot project at an exit-and-entry port in Shanghai.