UAV Based Imaging Platform for Monitoring Maize Growth Throughout Development

Abstract

Plant height (PH) data collected at high temporal resolutions can give insight into important growth parameters useful for identifying elite material in plant breeding programs and developing management guidelines in production settings. However, in order to increase the temporal resolution of PH data collection, more robust, rapid and low-cost methods are needed to evaluate field plots than those currently available. Due to their low cost and high functionality, unmanned aerial vehicles (UAVs) can be an efficient means for collecting height at various stages throughout development. We have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery and have used this platform to characterize a yield trial consisting of 24 maize hybrids planted in replicate under two dates and three planting densities in St Paul, MN in the summer of 2018. The field was imaged weekly after planting using a DJI Phantom 4 Advanced drone to extract PH and hand measurements were collected following aerial imaging of the field. In this work, we test the error in UAV PH measurements and compare it to the error obtained within manually acquired PH measurements. We also propose a method for improving the correspondence of manual and UAV measured height and evaluate the utility of using UAV obtained PH data for assessing growth of maize genotypes and for estimating end-season height.

Plant phenotyping relevance

UAV画像とSfMを用いたトウモロコシ草丈抽出法の開発、手測定との誤差比較・検証、育種試験への実質的な適用が中心である。

Code and data availability

The paper explicitly states that the authors' custom MATLAB image-analysis and trait-extraction scripts for UAV-derived plant height are publicly available in a GitHub repository. No phenotype dataset or imagery deposit is stated in the supplied blocks.