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ARS Home » Plains Area » Stillwater, Oklahoma » Wheat, Peanut, and Other Field Crops Research » Research » Research Project #431450

Research Project: Remote Sensing by UAV to Detect Sugarcane Aphid in Sorghum

Location: Wheat, Peanut, and Other Field Crops Research

Project Number: 3072-22000-016-31-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Sep 15, 2016
End Date: Dec 31, 2017

The objective of this project is to develop UAV based multispectral remote sensing technology for monitoring of sugarcane aphid infestations at sub-field and whole field scales.

The ARS scientist has developed a process using spatial pattern analysis to quantify the spatial pattern of aphid induced stress in agricultural fields. Statistical models are used to differentiate fields as infested by the target aphid or not infested based on the observed values of spatial pattern metrics. The method has been successful for distinguishing wheat fields infested with aphids, and preliminary studies indicate that a similar approach will work for sorghum fields infested with sugarcane aphid (SCA). Thus, a process will be developed for using airborne multispectral data to detect and monitor SCA infested fields at multiple spatial scales. In this NACA, we will integrate a miniaturized multi-spectral camera into a flight platform capable of precisely maintaining flight attitude, roll, pitch, heading, and location. It will be possible to provide a precise geo-referencing solution for each image and to generate a mosaicked image of an entire sorghum field based on a single flight. The development of the visualization module will include the following tasks: 1) accepting data from the UAS platform, 2) presenting the data in different formats and perspectives; for example, temporal and spatial, 3) ingesting relevant historical, contemporary, and real-time information, and 4) integrating relevant analytical frameworks into the system for knowledge extraction. The team has extensive expertise in spatial analytics, open-source GIS, and visualization, which will be leveraged to develop a cost effective alternative to manned aircraft for obtaining field scale multispectral remote sensing data, which can analyzed to detect SCA infestations.