This paper presents the methods to increase the accuracy of unmanned aerial vehicles (UAV)-based remote sensing data for the determination of plant nitrogen and water stresses with increased accuracy. As the demand for agricultural products is significantly increasing to keep up with the growing population, it is important to investigate methods to reduce the use of water and chemicals for water conservation, reduction in the production cost, and reduction in environmental impact. UAV-based remote sensing techniques can help significantly reduce the amount of water and nitrogen applications for crop production. The main advantage of UAV-based remote sensing technique is the immediate availability of high-resolution data that can be used to determine the crop performances and stresses of a large area in a short amount of time throughout the growth season for precision agriculture, which aims to optimize the amount of water, fertilizers, and pesticides using site-specific management of crops.

However, to be useful in a meaningful way for precision agriculture, the remote sensing data must provide the crop nitrogen and water stresses very accurately. To increase the accuracy of remote sensing data, various methods are being investigated. These include use of portable Ground Control Points, high performance GPS, and RTK GNSS receivers and base stations.   Remote sensing data was collected from UAVs equipped with hyperspectral and multispectral sensors. The paper will show the results for strawberry plants. Also, the effect of altitude in the accuracy of remote sensing data is being investigated. 

To determine the effectiveness of the above methods, vegetation indices such as normalized difference vegetation index (NDVI), Green NDVI, (GNDVI), and Water Band Index (WBI) calculated using the remote sensing data were compared with the data obtained from proximal sensors that include Handheld Spectroradiometer, Water Potential Meter, and Chlorophyll Meter. Correlations between different vegetation indices, chlorophyll meter data, and spectroradiometer data will be shown for strawberry plants.