Assessing the Impact of UAV Flight Altitudes on the Accuracy of Multispectral Indices

Summary Precision agriculture has increasingly incorporated the use of Unmanned Aerial Vehicles (UAVs) equipped with multispectral cameras. This study examined the influence of different UAV flight altitudes on the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Red Edge (NDRE), Optimized Soil-Adjusted Vegetation Index (OSAVI), and Leaf Chlorophyll Index (LCI), indices critical to crop monitoring and health assessment. The experiment was conducted on a 2-hectare winter wheat field at the Institute of Field and Vegetable Crops in Novi Sad, Serbia. The field was divided into 400 plots, each containing different wheat varieties subjected to twenty distinct combinations of artificial mineral fertilizer (NPK) treatments. A DJI P4 Multispectral drone was employed to capture images at altitudes of 30, 60, and 90 meters on three separate dates, corresponding to different plant growth stages: May 9, May 20, and June 6, 2022. All other operating parameters were held constant. The data were processed using the DJI Terra and Pix4D software to generate orthomosaic maps, which were subsequently analyzed using ArcGIS (v10.5, ESRI, Redlands, CA, USA) to calculate the multispectral index values for each plot. The results were statistically analyzed using the STATISTICA Tibco software. The analysis revealed significant differences in the index values based on the UAV flight altitude (p < 0.05). This research underscores the centrality of selecting the optimal UAV flight altitude to ensure the accuracy and reliability of data. While higher altitudes enable UAVs to cover larger areas in a single flight, factors such as image resolution, wind conditions, and the precision of crop health indicators must be considered. These findings offer valuable insights for agricultural professionals seeking to improve crop monitoring and ultimately enhance agricultural productivity through more effective UAV deployment.