Latterly, science tries to project the state of novel trends related to Industry 4.0 technologies. They are also accepted as the key enablers to reach a circular economy. Scientists search to model and understand the trajectory of developing technologies in the circular economy. Decision-making methodologies are one of the beneficial instruments to understand future topics. Multi-criteria decision-making (MCDM) methods can be helpful to assess and evaluate the developing technologies. Prospective multi-attribute decision-making (PMADM) is a subpart of MCDM approaches. It enables decisions and evaluation based on future aspects. In this paper, the focused area is the smart agriculture subject. The idea of smart agriculture has accelerated with the penetration of Industry 4.0 technologies into traditional agriculture. It is still an emerging and wide-open area thanks to the new application approaches of 4.0 technologies. Consequently, in this paper, a linguistic framework is proposed to evaluate sensor technologies in smart agriculture. Sensor technologies are one of the main components to achieve effective end-to-end control over the whole agrarian system. A group decision-making (GDM) approach is recommended with the novel Measurement of Alternatives and Ranking according to the Compromise solution (MARCOS) method. Plus, the MARCOS method is extended with the 2-tuple linguistic model for the first time to emphasize its ability to deal with multi-granular linguistic information. The 2-Tuple approach increases the accuracy of the linguistic computations without the loss of information and it provides the interpretability of the results. The evaluation criteria for sensor technologies are generated from a comprehensive literature review, and a case study of the proposed methodology is given. Plus, a comparative analysis is presented to show the robustness of the 2-tuple integrated MARCOS methodology.