Agriculture has been one of the most under-investigated areas in technology, and the development of Precision Agriculture (PA) is still in its early stages. This paper proposes a data-driven methodology on building PA solutions for collection and data modeling systems. Soil moisture, a key factor in the crop growth cycle, is selected as an example to demonstrate the effectiveness of our data-driven approach. On the collection side, a reactive wireless sensor node is developed that aims to capture the dynamics of soil moisture using MicaZ mote and VH400 soil moisture sensor. The prototyped device is tested on field soil to demonstrate its functionality and the responsiveness of the sensors. On the data analysis side, a unique, site-specific soil moisture prediction framework is built on top of models generated by the machine learning techniques SVM (support vector machine) and RVM (relevance vector machine). The framework predicts soil moisture n days ahead based on the same soil and environmental attributes that can be collected by our sensor node. Due to the large data size required by the machine learning algorithms, our framework is evaluated under the Illinois historical data, not field collected sensor data. It achieves low error rates (15%) and high correlations (95%) between predicted values and actual values across 9 different sites when forecasting soil moisture about 2 weeks ahead. Also, it is shown that the prediction outputs can remain accurate over a long period of time (one year) when reliable data are fed to the model every 45 days.