17 May 2016 Corn and sorghum phenotyping using a fixed-wing UAV-based remote sensing system
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Abstract
Recent development of unmanned aerial systems has created opportunities in automation of field-based high-throughput phenotyping by lowering flight operational cost and complexity and allowing flexible re-visit time and higher image resolution than satellite or manned airborne remote sensing. In this study, flights were conducted over corn and sorghum breeding trials in College Station, Texas, with a fixed-wing unmanned aerial vehicle (UAV) carrying two multispectral cameras and a high-resolution digital camera. The objectives were to establish the workflow and investigate the ability of UAV-based remote sensing for automating data collection of plant traits to develop genetic and physiological models. Most important among these traits were plant height and number of plants which are currently manually collected with high labor costs. Vegetation indices were calculated for each breeding cultivar from mosaicked and radiometrically calibrated multi-band imagery in order to be correlated with ground-measured plant heights, populations and yield across high genetic-diversity breeding cultivars. Growth curves were profiled with the aerial measured time-series height and vegetation index data. The next step of this study will be to investigate the correlations between aerial measurements and ground truth measured manually in field and from lab tests.
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Yeyin Shi, Yeyin Shi, Seth C. Murray, Seth C. Murray, William L. Rooney, William L. Rooney, John Valasek, John Valasek, Jeff Olsenholler, Jeff Olsenholler, N. Ace Pugh, N. Ace Pugh, James Henrickson, James Henrickson, Ezekiel Bowden, Ezekiel Bowden, Dongyan Zhang, Dongyan Zhang, J. Alex Thomasson, J. Alex Thomasson, } "Corn and sorghum phenotyping using a fixed-wing UAV-based remote sensing system", Proc. SPIE 9866, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, 98660E (17 May 2016); doi: 10.1117/12.2228737; https://doi.org/10.1117/12.2228737
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