16 July 2018 Phenotyping of sorghum panicles using unmanned aerial system (UAS) data
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Abstract
Unmanned Aerial System (UAS) is getting to be the most important technique in recent days for precision agriculture and High Throughput Phenotyping (HTP). Attributes of sorghum panicle, especially, are critical information to assess overall crop condition, irrigation, and yield estimation. In this study, it is proposed a method to extract phenotypes of sorghum panicles using UAS data. UAS data were acquired with 85% overlap at an altitude of 10m above ground to generate super high resolution data. Orthomosaic, Digital Surface Model (DSM), and 3D point cloud were generated by applying the Structure from Motion (SfM) algorithm to the imagery from UAS. Sorghum panicles were identified from orthomosaic and DSM by using color ratio and circle fitting. The cylinder fitting method and disk tacking method were proposed to estimate panicle volume. Yield prediction models were generated between field-measured yield data and UAS-measured attributes of sorghum panicles.
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A. Chang, A. Chang, J. Jung, J. Jung, J. Yeom, J. Yeom, M. Maeda, M. Maeda, J. Landivar, J. Landivar, "Phenotyping of sorghum panicles using unmanned aerial system (UAS) data", Proc. SPIE 10664, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, 106640B (16 July 2018); doi: 10.1117/12.2305099; https://doi.org/10.1117/12.2305099
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