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Over the last few years, multispectral and thermal remote sensing imagery from unmanned aerial vehicles (UAVs) has found application in agriculture and has been regarded as a means of field data collection and crop condition monitoring source. The integration of information derived from the analysis of these remotely sensed data into agricultural management applications facilitates and aids the stakeholder’s decision making. Whereas agricultural decision support systems (DSS) have long been utilised in farming applications, there are still critical gaps to be addressed; as the current approach often neglects the plant’s level information and lacks the robustness to account for the spatial and temporal variability of environmental parameters within agricultural systems. In this paper, we demonstrate the use of a custom built autonomous UAV platform in providing critical information for an agricultural DSS. This hexacopter UAV bears two cameras which can be triggered simultaneously and can capture both the visible, near-infrared (VNIR) and the thermal infrared (TIR) wavelengths. The platform was employed for the rapid extraction of the normalized difference vegetation index (NDVI) and the crop water stress index (CWSI) of three different plantations, namely a kiwi, a pomegranate, and a vine field. The simultaneous recording of these two complementary indices and the creation of maps was advantageous for the accurate assessment of the plantation's status. Fusion of UAV and soil scanner system products pinpointed the necessity for adjustment of the irrigation management applied. It is concluded that timely CWSI and NDVI measures retrieved for different crop growing stages can provide additional information and can serve as a tool to support the existing irrigation DSS that had so far been exclusively based on telemetry data from soil and agrometeorological sensors. Additionally, the use of the multi-sensor UAV was found to be beneficial in collecting timely, spatio-temporal information for the fusion with ground-based proximal sensing data. This research work was designed and deployed in the frame of the project "AGRO_LESS: Joint reference strategies for rural activities of reduced inputs".
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