Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop

The objective of this study is to determine the capability of an unmanned aerial vehicle system carrying a multispectral sensor to acquire multitemporal images during the growing season of a sunflower crop. Measurements were made at different times of the day and with different resolutions to estimate the normalised difference vegetation index (NDVI) and study its relationship with several indices related to crop status with the aim of generating useful information for application to precision agriculture techniques. NDVI was calculated from images acquired on four different dates during the cropping season. On two of these dates, two images were acquired to determine how the time of day when the images were taken influences NDVI value. To study the influence of image resolution on NDVI, the original images were resampled to 30 x 30 and 100 x 100 cm pixel sizes. The results showed that the linear regressions between NDVI and grain yield, aerial biomass and nitrogen content in the biomass were significant at the 99% confidence level, except during very early growth stages, whereas the time of day when the images were acquired, the classification process, and image resolution had no effect on the results. The methodology provides information that is related to crop yield from the very early stages of growth and its spatial variability within the crop field to be harvested, which can subsequently be used to prescribe the most appropriate management strategy on a site-specific basis. (C) 2015 IAgrE. Published by Elsevier Ltd. All rights reserved.