In this work, a new element of our research for autonomous plant phenotyping is presented: a simulated environment for development and testing. As explained in our previous work, our architecture consists of two robotic platforms: an autonomous ground vehicle (Vinobot) and a mobile observation tower (Vinoculer). The ground vehicle collects data from individual plants, while the observation tower oversees an entire field, identifying specific plants for further inspection by the ground vehicle. Indeed, while real robotic platforms for field phenotyping can only be deployed during the planting season, simulated platforms can help us to improve the various algorithms throughout the year. In order to do that, the simulation must be designed to mimic not only the robots, but also the field with all its uncertainties, noises and other unexpected circumstances that could lead to errors in those same algorithms under real conditions. This paper details the current state in the implementation of such simulation. It describes how the target navigation algorithms are being tested and it provides the first insights on the functionality of the simulation and its usefulness for testing those same robotic platforms.