In this study, we propose a novel technique for imaging of crops. Related work has significant limitations in terms of resolution as well as coverage in space and time. In order to overcome these limitations, we use a dense sampling of high-resolution images, which enables the creation of a sequence of 3D reconstructions over time, i.e., a 4D spatio-temporal model of a crop as it develops. To this end, we mounted a CCD camera with integrated GPS and Inertial Measurement Unit (IMU) on a ground articulated tractor, and we collected a set of images approximately once per week over staggered plantings of broccoli from October to January in South Georgia. Multiple images of individual plants taken from different angles are stitched together to produce a 3D reconstruction each week, using "Structure from Motion" (SFM) techniques from computer vision. These 3D reconstructions are then combined in a 4D GIS to show the plants growth and production of fruit over time (4D). These 4D imaging techniques can be applied to images collected from autonomous ground or aerial vehicles for multiple site-specific management practices, such as estimating where, when and how many fruit are ready for harvest in certain vegetable and fruit crops. Other potential applications include record keeping of canopy development for orchards, measuring rate of change of leaf area index, and weed dispersal, proliferation and identification.