Various oil seed and cereal grain products are stored by farmers and commercial manufacturers each year, world wide. However, it is while in storage that these products are most susceptible to quality degradation or even spoilage. Present detection methods, such as random sampling, are not practical for preventing such events during storage. Therefore, our system was designed to rectify this occurrence. The system is a conglomerate of real-time monitoring and data management. Utilizing a sensor network, streaming data are analyzed to determine the conditions of the stored product and decide if any corrective action is needed, such as aeration. The system also affords the functionality of storing and querying historical data. This thesis explains how various computer science concepts were pooled together to construct this real-life application.