With supervised machine learning algorithms, meaningful information can be extracted from satellite images to support rice farm mapping. The success of these algorithms depends largely on the availability and quality of ground-truth reference data. However, collecting such data is often laborious and time-consuming. The fast development of drone technique has opened up an efficient way for ground truthing. In this study, we construct a drone-based sensing system for the purpose of efficiently collecting ground-truth data. The drone carries dual cameras that provide multispectrsal images with high spatial resolution in the same sensed site. The system is dedicated to collecting training data for rice farm mapping in Australia. To demonstrate the ability of the constructed system, real-flight experiments were conducted. Drone images of rice crops were acquired in the Riverina region of Australia, during the 2018-2019 summer season. Three-dimensional models were constructed from multiple images captured by the drone, where the structural information of rice crops was extracted. Results show that the configuration of dual cameras and the construction of three-dimensional models are particularly advantageous for ground truthing, providing valuable information for reliably identifying rice crops.