This paper shows the comparison between multispectral and hyperspectral data collected from UAVs in detecting citrus nitrogen and water stresses. UAVs equipped with multispectral and hyperspectral sensors were flown over Citrus trees at Cal Poly Pomona’s Spadra Farm. The multispectral and/or hyperspectral data are used in the determination of normalized differential vegetation index (NDVI), water band index (WBI), and other vegetation indices. These indices are compared with the proximal sensor data that include handheld spectroradiometer, water potential meter, and chlorophyll meter. Correlations of multispectral and hyperspectral data with the proximal sensor data are shown.
This paper presents the ground-truthing of remote sensing data of citrus plants collected from unmanned aerial vehicles (UAVs). The main advantage of the UAV-based remote sensing is the reduced cost and immediate availability of high resolution data. This helps detect crop stresses throughout the crop season. Near infrared (NIR) images obtained using remote sensing techniques help determine the crop performances and stresses of a large area in a short amount of time for precision agriculture, which aims to optimize the amount of water, fertilizers, and pesticides using site-specific management of crops. However, to be useful for the real-world applications, the accuracy of remote sensing data must be validated using the proven ground-based methods. UAVs equipped with multispectral sensors were flown over the citrus orchard at Cal Poly Pomona’s Spadra Farm. The multispectral/hyperspectral images are used in the determination of vegetation indices that provide information on the health of the plant. Handheld spectroradiometer, water potential meter, and chlorophyll meter were used to collect ground-truth data. Correlations between the vegetation indices calculated using airborne data and proximal sensor data are shown.