Pengcheng Hu1, Bangyou Zheng1*, Scott Chapman1, 2
1 CSIRO Agriculture and Food, Queensland Biosciences Precinct 306 Carmody Road, St Lucia, 4067, QLD, Australia,
2 School of Agriculture and Food Sciences, The University of Queensland, Gatton QLD 4343, Australia, * Corresponding Author: Tel: +61 (0)7 3214 2620 email: Bangyou.Zheng@csiro.au
With advances in camera and robotic technologies, unmanned aerial vehicles (UAV) provide the means to capture images at sufficiently large scale to extract crop phenotypic traits. The current challenges are to manage efficiently the meta-information acquired, involving the processing of large stores of images, visualising intermediate and final outputs, and estimating phenotypic traits with robust algorithms. A cloud-based platform, PhenoCopter, was developed to handle the challenge of data processing. Here we demonstrate how this UAV based platform can enable scientists to extract phenotypic traits including ground coverage, canopy height, aboveground biomass and leaf area index (LAI). Across crops and situations, the UAV-based platform can be shown to be reliable for estimating phenotypic traits.