Background: Data driven sustainable agriculture involves the collection, storage, processing, and analysis of enormous spatiotemporal data related to crop production processes, systems, infrastructure, and environment. Literature survey on smart agriculture research projects indicates that there is a need for improving handling of spatiotemporal data. There are gaps in handling spatial and temporal variability of parameters, division of crop field into management zones to reduce the variability for optimizing application of inputs such as fertilizers, water or pesticides. These gaps are to be addressed at design level of spatiotemporal database for smart agriculture. Objective: To engineer a spatiotemporal database schema that can be used for data driven sustainable agriculture. Methods: The methodology involves spatiotemporal data representation and modeling, Object oriented analysis and design of the database and Verification of the database using life cycle model and algorithmic steps. Findings: The resulting database is capable to store spatial and temporal variability of soil, plant and water parameters as well as handle spatial split, spatial merge, geometry and location changes of spatiotemporal objects. Novelty: Novel Use cases in smart agriculture along with spatiotemporal attributes are identified so that efficient applications can be realized. Adaption of the spatiotemporal database schema for Smart Irrigation System and its implementation methodology are presented.