Small plot identification from video streams for high-throughput phenotyping of large breeding populations with unmanned aerial systems

Xu Wang; Cameron Amos; Mark Lucas; Grant Williams; Jesse Poland

doi:10.1117/12.2518532

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14 May 2019 Small plot identification from video streams for high-throughput phenotyping of large breeding populations with unmanned aerial systems

Xu Wang, Cameron Amos, Mark Lucas, Grant Williams, Jesse Poland

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Proceedings Volume 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV; 110080D (2019) https://doi.org/10.1117/12.2518532
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, Maryland, United States

Abstract

In breeding and genetics programs, thousands of small plots in the size of a few square meters each having a unique genetic entry, are used to evaluate huge numbers of candidates and large mapping populations. Low-altitude remote sensing with unmanned aerial systems (UAS) can generate high geospatial resolution measurements of plants and enable high temporal resolution measurements through multiple crop growth stages. However, to identify individual plot from aerial images robustly and automatically becomes a key challenge in high throughput phenotyping (HTP) using UAS. In this case study, we captured super high-resolution video clips of wheat canopies by a UAS at low altitude. We proposed an image processing pipeline for identifying individual plot from video frames. Preliminary results indicate the methods can highly accelerate the process of linking genotypes to individual plot images and can be fully automated. This research provides a proof-of-concept and has broad implications of novel phenomics application of UAS that is scalable to tens-of-thousands of plots in crop breeding and genetic studies.

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Xu Wang, Cameron Amos, Mark Lucas, Grant Williams, and Jesse Poland "Small plot identification from video streams for high-throughput phenotyping of large breeding populations with unmanned aerial systems ", Proc. SPIE 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, 110080D (14 May 2019); https://doi.org/10.1117/12.2518532

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