Agrivoltaics is defined as “the dual use of land for solar energy production and agriculture”. On this topic, a number of issues are still to be properly addressed, to understand e.g. how the shading effect of the solar panels affects crop growth. In this work, the development of a large-scale digital twin model to predict crop yield under a varying solar panel coverage is discussed. A framework is proposed to exploit Internet of Things (IoT) concepts, with a sensor network to collect data on the field, merged with sensor fusion to also handle information gathered by satellite images. The aim of the entire work being related to the synergic optimization of energy production and crop yield, data analytics based on artificial intelligence tools are to be extensively developed. Results are reported of an experimental activity, currently under way at the Fantoli laboratory of Politecnico di Milano. Wooden panels, placed above the crop with varying orientation and pattern, are used to study the aforementioned shading effect with a specific target on conditions typical of Northern Italy. The laboratory facility is equipped with a comprehensive sensor network, to acquire the data necessary to build the targeted large-scale digital twin of the agrivoltaic system.