Unmanned Aerial Vehicle to Estimate Nitrogen Status of Turfgrasses
Authors: Lisa Caturegli, Matteo Corniglia, Monica Gaetani, Nicola Grossi, Simone Magni, Mauro Migliazzi, Luciana Angelini, Marco Mazzoncini, Nicola Silvestri, Marco Fontanelli, Michele Raffaelli, Andrea Peruzzi, Marco Volterrani
Publisher: PLOS One
Spectral reflectance data originating from Unmanned Aerial Vehicle (UAV) imagery is a valuable tool to monitor plant nutrition, reduce nitrogen (N) application to real needs, thus producing both economic and environmental benefits. The objectives of the trial were i) to compare the spectral reflectance of 3 turfgrasses acquired via UAV and by a ground-based instrument; ii) to test the sensitivity of the 2 data acquisition sources in detecting induced variation in N levels. N application gradients from 0 to 250 kg ha-1 were created on 3 different turfgrass species: Cynodon dactylon x transvaalensis (Cdxt) ‘Patriot’, Zoysia matrella (Zm) ‘Zeon’ and Paspalum $*@!tum (Pv) ‘Salam’. Proximity and remote-sensed reflectance measurements were acquired using a GreenSeeker handheld crop sensor and a UAV with onboard a multispectral sensor, to determine Normalized Difference Vegetation Index (NDVI). Proximity-sensed NDVI is highly correlated with data acquired from UAV with r values ranging from 0.83 (Zm) to 0.97 (Cdxt). Relating NDVI-UAV with clippings N, the highest r is for Cdxt (0.95). The most reactive species to N fertilization is Cdxt with a clippings N% ranging from 1.2% to 4.1%. UAV imagery can adequately assess the N status of turfgrasses and its spatial variability within a species, so for large areas, such as golf courses, sod farms or race courses, UAV acquired data can optimize turf management. For relatively small green areas, a hand-held crop sensor can be a less expensive and more practical option.
Photo: The UAV during flight operations and a detail of the vehicle DJI S1000 Octocopter. (Credits: Lisa Caturegli, Matteo Corniglia, Monica Gaetani, Nicola Grossi, Simone Magni, Mauro Migliazzi, Luciana Angelini, Marco Mazzoncini, Nicola Silvestri, Marco Fontanelli, Michele Raffaelli, Andrea Peruzzi, Marco Volterrani) https://adalidda.net/posts/fTsExnEh6C9kz23gw/unmanned-aerial-vehicle-to-estimate-nitrogen-status-of