Cities grow exponentially at the same time as the demand for food, needing to increase the production of supplies and its costs due to farmers having to apply chemicals and do genetic modifications to harvest to respond to this demand. There are some technologies to get information from the harvest through devices that can capture hyperspectral images, allowing them to see things we cannot with our vision. They are used to get information from crops, like plant health and vitality, plant stress, disease and pest detection, soil cover, and crop maturity. However, due to the cost of hyperspectral cameras and their lack of spatial resolution, this paper aims to explore models for getting those images from RGB images obtained with more common and cheaper cameras.