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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #304987
Title: Detection of nitrogen deficiency in potatoes using unmanned aircraft systems

Author
item Hunt Jr, Earle
item HORNECK, D. - Oregon State University
item HAMM, P. - Oregon State University
item GADLER, D. - Collaborator
item BRUCE, A. - Collaborator
item TURNER, R. - Collaborator
item SPINELLI, C. - Collaborator
item BRUNGARDT, J. - Collaborator

Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 6/3/2014
Publication Date: 6/17/2014
Citation: Hunt Jr, E.R., Horneck, D., Hamm, P., Gadler, D., Bruce, A., Turner, R., Spinelli, C., Brungardt, J. 2014. Detection of nitrogen deficiency in potatoes using unmanned aircraft systems. Proceedings of the 12th International Conference on Precision Agriculture. Available: https:/www.ispag.org/programs/2/1431/paper_1431.pdf.

Interpretive Summary:

Technical Abstract: Small Unmanned Aircraft Systems (sUAS) are recognized as potentially important remote-sensing platforms for precision agriculture. We set up a nitrogen rate experiment 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 sensor were used to collect imagery with near-infrared (NIR), red and green bands with pixel sizes from 2.0 to 3.0 cm. Petiole nitrate concentration, leaf chlorophyll content, and leaf area index (LAI), were measured twice during the growing season; plants in the two lower N application treatments had lower petiole nitrate, chlorophyll content, LAI, and vegetation cover as expected. Three spectral indices were calculated from the color-infrared imagery (NDVI, GNDVI, and NGRDI); in late June, NDVI and GNDVI were correlated to LAI and cover, but not leaf chlorophyll content. In early August, the spectral indices were also correlated to chlorophyll content. Differences in vegetation cover may be detectable earliest, simply by counting the number of pixels with high NDVI in a small area. These results suggest sUAS sensors with higher spatial resolution would be more useful for site specific nitrogen management.