The agriculture sector is fundamental for social, economic, and environmental development. It needs novel approaches and technology-integrated processes to preserve its critical importance and survive for the future. Agricultural digitalization is an essential component of agricultural industrialization, focusing on agricultural research, infrastructural improvements, and data services. The combination of the Internet of Things/Everything (IoT/IoE) with RFID, sensors, and high-tech meters makes up smart agriculture (SA). Controlling and monitoring have become more easily applicable thanks to these technological improvements. SA replaces conventional farming methods with effective, rapid, and sustainable ones. It has the power to control water, pesticides, security, the environment, machines, and vehicles. Digital Twin (DT) technology is the mutual use of digital technologies such as remote sensing, IoT, and simulation. With its integrated structure, DT can help farmers to create a virtual twin of their physical entities in the virtual space. Accordingly, generating strategies and planning the production can be controlled by running simulations with the field's collected data. Therefore, this paper aims to investigate challenges to DT adoption in SA. For that purpose, a multicriteria decision-making (MCDM) approach is suggested. DEMATEL technique is provided to prioritize and evaluate causal relationships for DT adoption challenges. The DEMATEL technique is integrated with the 2-Tuple Linguistic (2-TL) model to improve its ability to deal with linguistic variables and create a decision-making process closer to human cognitive processes. A real case study is provided to test the applicability of the suggested methodology, and further discussions are presented.