Precision agriculture is a practical approach to maximize crop yield with optimal use of rapidly depleting natural resources. Availability of specific and high resolution crop data at critical growth stages is a key for real-time data driven decision support for precision agriculture management during the production season. The goal of this study was to evaluate the feasibility of using small unmanned aerial system (UAS) integrated remote sensing tools to monitor the abiotic stress of eight irrigated pinto beans (Phaseolus vulgaris L.) with varied irrigation and tillage treatments. A small UAS integrated with a multispectral and an infrared thermal imaging camera was used to collect data of bean field plots on 54, 76 and 98 days after planting (DAP). Indicators such as green normalized vegetation index (GNDVI), canopy cover (CC, ratio of ground covered by crop canopy to the total plot area) and canopy temperature (CT, °C) of crops were extracted from imaging data of the two types of sensor. The statistical difference of the developed indictors in crops with different treatments was analyzed to show their performance in detecting crop stress. The indicators and their combinations of temporal data were also correlated with ground reference yield data to validate the effectiveness in stress monitoring. Results show that the GNDVI, CC and CT were able to differentiate crop grown under full and deficit irrigation treatments at each of the three growth stages. The developed indicators were strongly correlated with crop yield with Pearson correlation coefficients (r) of 0.71 and 0.72 for GNDVI and CC, respectively, in the early growth stage (54 DAP). Canopy temperature also showed high correlation with yield with r of 0.84 at 76 DAP and 0.77 at 98 DAP. Performance of small UAS based indicators in crop yield estimation was improved substantially when temporal data of each indicator were used for correlation. Overall, the small UAS based remote sensing tool has the potential in rapid crop stress monitoring and management.