The application of big data solutions to village-level smart farms in the Philippines may not happen immediately. The low-fidelity prototype developed in this study should represent a little but determined step closer to such a lofty aspiration. The study aimed to develop an information system highlighting the application of Big Data solutions to village-level smart farms in the Philippines. In the systems analysis phase, the study engaged potential early adopters and cross-referenced their needs with available technologies. Modeling tools and techniques such as the HIPO Chart, Systems Flow Chart, Use Case Diagram, and Data Flow Diagram highlighted the systems design phase. Defining the elements of the Data Stores explained the application of Big Data in optimizing crop yields forecast. Village-level data on yield estimate, vegetation indices, pest, and disease monitoring, and the relevant analytics can be generated from drone-based, data vegetation indices, and data from sensors and drones Farmer-level data are consolidated into Clusters, and subsequently, into Groups. Big data strengthened crop yield forecasting to provide farmers with the best option for the next crop while performing analytics to generate highly visual dashboards. The system modules developed for the Farmer, Cluster Technician, and Group Manager proved that Big Data solutions can be applied to village-level smart farms in the Philippine setting in improving crop productivity. A similar system should find useful applications in the equivalent operational areas in the poultry, livestock, and aquaculture industries. The process of systems analysis and design employed in the study likewise provides instructional value.