Improving precision N fertilization application in onion fields using UAV proximal sensing imagery.

Remote and proximal sensing technologies are valuable tools to gather the field variability and adopt precision agriculture (PA) technologies at high resolution. This study attempts to discover the appropriate level of nitrogen (N)-based fertilisers for improving the productivity of “Borettana” onion cultivar in a field experiment by coupling unmanned aerial vehicles (UAV)-NDVI imagery with field surveys. Following an experiment that involved the setup of 20 plots in the onion field in a randomised complete block design (RCBD), with 5 replications (2 sub- replications), 4 distinct levels of N application were compared: N160 (160 kg N ha -1 ), N180 (180 kg N ha -1 ), N200 (200 kg N ha -1 ) and N220 (220 kg N ha -1 ). The crop cycle was followed utilizing multispectral UAV imaging, which provided NDVI maps in different time steps Results showed that onion bulb biomass did not different between treatments, while the apparent N use efficiency increased significantly in the N160 vs. N200 and N220 treatments. NDVI mapping was compared with both belowground and aboveground onion biomass and N-content, showing that remote sensing was able to detect variability of onion growth until the NDVI index saturation was reached, c.a. 45 days before harvest. data. Further monitoring studies are required to validate the obtained results and suggest fine estimates of N input in Borettana onion based on NDVI-UAV imagery.