Remote sensing is a method of monitoring the natural heterogeneity of vegetation. Although satellite based remote sensing has been a popular method for monitoring the earth's surface, it has several drawbacks, such as the orbital period, unattended capture, and investment cost. On the other hand, an unmanned air vehicle (UAV) is more flexible in terms of deployment, monitoring a small area, and being easy to obtain at a low cost. From this point of view, the goal of this research was to develop a low cost and easy to implement technical solution for mapping spatial heterogeneity and research its relationship with plant conditions. The intention was to develop a cycling process starting with a UAV-based image-capturing tool for an easy and reasonable production of a normalized difference vegetation index (NDVI) and the resulting prescription maps, especially of vineyards. The main parts of this image acquisition system were the UAV and modified digital cameras purchased from the store. Two different fixed-winged UAVs were built for this study based on commercial airplane models and used open source autopilot. Two small digital cameras (Nikon and Canon) were tested for capturing the images. These were modified to capture electromagnetic energy ranging from 380 nm to 1100 nm. Camera calibration tests were conducted and a UAV-based image acquisition system was successfully developed. In the next step, future field tests will be conducted to assess the practical usage of running the cycling process.