The ever-increasing incidence and severity of agricultural diseases pose a threat to the safety of food supplies around the world. In the following chapter, we will investigate how the Digital Twin (DT) technology can be utilized in the process of developing predictive disease detection models for agricultural purposes. Through the use of Internet of Things (IoT) sensors, meteorological stations, drones, and satellite photography, digital twins are able to generate virtual duplicates of crops that are in constant alignment with accurate real-time data. In order to mimic disease transmission patterns and find early symptoms of epidemics, digital twins can be utilized to incorporate this data through the utilization of sophisticated analytics and machine learning approaches. This chapter takes a look at some of the most important components of predictive modeling, such as the connection between viruses, surroundings, and hosts, the diagnosis of sickness using imaging, and the evaluation of risk within the context of a decision tree framework. This includes the presentation of actual case studies that illustrate the identification of rust in wheat, blast in rice, and late blight in potatoes. In addition, they illustrate the appropriate use of the models and the responses of farmers that correspond to those models. The chapter comes to a close with an analysis of the technical issues, which include the integration of data and the generalization of models, the hurdles of attaining user approval, and the potential applications of decision trees in intelligent crop disease surveillance systems that are scalable.