75-5 Remote Sensing Using Small Unmanned Aircraft Systems for Detection of Nitrogen Deficiency in Potatoes.

See more from this Division: ASA Section: Climatology & Modeling
See more from this Session: Symposium--Agricultural Remote Sensing with UAVs: Challenges and Opportunities
Monday, November 3, 2014: 3:15 PM
Long Beach Convention Center, Room 201B
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E. Raymond Hunt Jr.1, Donald A. Horneck2, Charles B. Spinelli3, Alan E. Bruce3 and Josh J. Brungardt4, (1)Hydrology and Remote Sensing Lab, USDA-ARS, Beltsville, MD
(2)Extension Agronomy, Oregon State University, Hermiston, OR
(3)The Boeing Company, Kent, WA
(4)Paradigm ISR, Bend, OR
Small Unmanned Aircraft Systems (sUAS) are recognized as potentially important remote-sensing platforms for precision agriculture. A nitrogen rate experiment was established in 2013 with ‘Ranger Russet’ potatoes by applying four rates of nitrogen fertilizer (112, 224, 337, and 449 kg N/ha) in a randomized block design with 3 replicates. A Tetracam Hawkeye sUAS and Agricultural Digital Camera Lite sensor were used to collect imagery with near-infrared (NIR), red and green bands with pixel sizes from 1 to 4 cm. Colored tarps were set out for each flight for an empirical calibration of digital numbers to spectral reflectances; however, the camera footprint was too small to have the tarps in each image. Two spectral indices were calculated from the color-infrared imagery, the normalized difference vegetation index (NDVI) and the green normalized difference vegetation index (GNDVI). NDVI and GNDVI from the tarp digital numbers were correlated to NDVI and GNDVI calculated from tarp spectral reflectances.  The slopes and intercepts of the calibration equations varied with the exposure time, which was set by the sensor. For images without the tarps, the exposure time was used to determine which calibration equation was used. Variation of NDVI and GNDVI over the growing season followed changes of leaf area index or plant cover. Comparison of GNDVI with NDVI was expected to enhance sensitivity to differences of leaf chlorophyll content; but only plots with the low N treatment were detectable. The value of sUAS for precision agriculture is information and its relevance to management. A first law of agricultural UAS is proposed, “it’s the sensor, not the platform.”
See more from this Division: ASA Section: Climatology & Modeling
See more from this Session: Symposium--Agricultural Remote Sensing with UAVs: Challenges and Opportunities