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Title: Insect detection and nitrogen management for irrigated potatoes using remote sensing from small unmanned aircraft systems

Author
item Hunt Jr, Earle
item RONDON, S.I. - Oregon State University
item HAMM, P. - Oregon State University
item TURNER, R. - Collaborator
item BRUCE, A. - Collaborator
item BRUNGARDT, J. - Collaborator

Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: 5/5/2016
Publication Date: 5/20/2016
Citation: Hunt Jr, E.R., Rondon, S., Hamm, P., Turner, R., Bruce, A., Brungardt, J. 2016. Insect detection and nitrogen management for irrigated potatoes using remote sensing from small unmanned aircraft systems. Proceedings of SPIE.APP:4/14/2016 SUBMIT:3/21/2016 PUB:5/20/2016 Vol. 9866, 98660N, DOI: 10.1117/12.2224139.

Interpretive Summary:

Technical Abstract: Remote sensing with small unmanned aircraft systems (sUAS) has potential applications in agriculture because low flight altitudes allow image acquisition at very high spatial resolution. We set up experiments at the Oregon State University Hermiston Agricultural Research and Extension Center with different platforms and sensors to assess advantages and disadvantages of sUAS for precision farming. In 2013, we conducted an experiment with 4 levels of N fertilizer, and followed the changes in the normalized difference vegetation index (NDVI) over time. In late June, there were no differences in chlorophyll content or leaf area index (LAI) among the 3 higher application rates. Consistent with the field data, only plots with the lowest rate of applied N were distinguished by low NDVI. In early August, N deficiency was determined by NDVI, but it was too late to mitigate losses in potato yield and quality. Populations of the Colorado potato beetle (CPB) may rapidly increase, devouring the shoots, thus early detection and treatment could prevent yield losses. In 2014, we conducted an experiment with 4 levels of CPB infestation. Over one day, damage from CPB in some plots increased from 0 to 19%. A visual ranking of damage was not correlated with the total number of CPB or treatment. Plot-scale vegetation indices were not correlated with damage, although the damaged area determined by object-based feature extraction was highly correlated. Methods based on object-based image analysis of sUAS data have potential for early detection and reduced cost.