Calibration of photogrammetry-based canopy profile mapping using dense, sUAS-based LiDAR data in almond orchards (Conference Presentation)

Kyle Cheung; Alireza Pourreza; Ali Moghimi; German Zuniga-Ramirez

doi:10.1117/12.2557895

23 April 2020 Calibration of photogrammetry-based canopy profile mapping using dense, sUAS-based LiDAR data in almond orchards (Conference Presentation)

Kyle Cheung, Alireza Pourreza, Ali Moghimi, German Zuniga-Ramirez

Author Affiliations +

Proceedings Volume 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V; 1141408 (2020) https://doi.org/10.1117/12.2557895
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Almond canopy geometry has been shown to correlate with harvest yield, but the existing specialized and expensive equipment used to measure geometric features provides data limited in resolution and must be operated in a narrow time window, challenging its role in precise orchard management. To increase adoption, this study examines novel aerial data collection methods by small unmanned aerial systems (sUAS) and intuitive data processing methods with the goal of improving accuracy and reducing cost, time, and training required for canopy measurements and potential yield estimation.

Conference Presentation

Citation Download Citation

Kyle Cheung, Alireza Pourreza, Ali Moghimi, and German Zuniga-Ramirez "Calibration of photogrammetry-based canopy profile mapping using dense, sUAS-based LiDAR data in almond orchards (Conference Presentation)", Proc. SPIE 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V, 1141408 (23 April 2020); https://doi.org/10.1117/12.2557895

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