409-22 Testing Capabilities and Applications of Unmanned Aerial Systems in Precision Agriculture.

Poster Number 121

See more from this Division: ASA Section: Agronomic Production Systems
See more from this Session: Agronomic Production Systems: II

Wednesday, November 18, 2015
Minneapolis Convention Center, Exhibit Hall BC

Joseph Taylor1, Robert Austin1, Joshua Heitman2, Deanna L. Osmond3, Carl R. Crozier4 and Alan D. Meijer5, (1)North Carolina State University, Raleigh, NC
(2)Crop and Soil Sciences, North Carolina State University, Raleigh, NC
(3)PO Box 7620, North Carolina State University, Raleigh, NC
(4)Soil Science, North Carolina State University, Plymouth, NC
(5)North Carolina State University, Plymouth, NC
Abstract:
Remote Sensing has long been used in precision agriculture to identify spatial variations in soil and crop conditions. Recently, Unmanned Aerial Systems (UAS) have emerged as a remote sensing platform able to collect aerial imagery at resolutions, costs, and frequencies previously unobtainable. In agriculture, timeliness of information is critical to effectively manage crop health. In particular, identifying nutrient stress before yield loss occurs. The timeliness of UAS allows the grower to take images of their fields and identify nutrient stressed areas prior to application. The objective of this project is to validate both color and multi-spectral imagery collected from an UAS platform using in-situ field measurements of plant nitrogen (N) concentrations, field spectroscopy, and a Trimble GreenSeeker. This past season, a fixed-wing (Fourth Wing – Vireo) UAS was used to collect visible and multi-spectral imagery of a winter wheat N-rate trial in Plymouth, North Carolina. Twelve strips (400 x 30 ft) were arranged in a randomized complete block design and had three starter N application rates (0, 20, 40 lbs N per acre) applied. At top-dress, each strip received an additional low, medium, and high rate of N (0, 75, 150 lbs N per acre). GreenSeeker, tissue samples, and spectrometer readings were taken to correspond with flights and followed a regular bi-weekly schedule before and after the top-dress application. Correlations between aerial images and ground measurements (i.e., vegetative indices, soil testing, tissue samples, and spectroscopy) will be used to determine the camera’s optical response to differing N treatments. Estimates of current crop N based on the UAS-captured imagery will be calculated by a vegetative index, normalized difference vegetation index. Initial results indicate a positive correlation between aerial imagery and ground measurements.

See more from this Division: ASA Section: Agronomic Production Systems
See more from this Session: Agronomic Production Systems: II