A UAV‐based high‐throughput phenotyping approach to assess time‐series nitrogen responses and identify trait‐associated genetic components in maize. Issue 1 (11th February 2022) - Rodene, Eric; Xu, Gen; Palali Delen, Semra; Zhao, Xia; Smith, Christine; Ge, Yufeng; Schnable, James; Yang, Jinliang
Abstract: Advancements in the use of genome‐wide markers have provided unprecedented opportunities for dissecting the genetic components that control phenotypic trait variation. However, cost‐effectively characterizing agronomically important phenotypic traits on a large scale remains a bottleneck. Unmanned aerial vehicle (UAV)‐based high‐throughput phenotyping has recently become a prominent method, as it allows large numbers of plants to be analyzed in a time‐series manner. In this experiment, 233 inbred lines from the maize ( Zea mays L.) diversity panel were grown in the field under different nitrogen treatments. Unmanned aerial vehicle images were collected during different plant developmental stages throughout the growing season. A workflow for extracting plot‐level images, filtering images to remove nonfoliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed. After applying the workflow, about 100, 000 plot‐level image clips were obtained for 12 different time points. High correlations were detected between VIs and ground truth physiological and yield‐related traits. The genome‐wide association study was performed, resulting in n = 29 unique genomic regions associated with image extracted traits from two or more of the 12 total time points. A candidate gene Zm00001d031997, a maize homolog of the Arabidopsis HCF244 ( high chlorophyll fluorescence 244 ), located underneath the leading single nucleotide polymorphisms of the canopy coverage associated signals were repeatedly detected under both nitrogen conditions. The plot‐level time‐series phenotypic data and the trait‐associated genes provide great opportunities to advance plant science and to facilitate plant breeding. Core Ideas: A workflow was developed to clip, filter, and analyze aerial RGB images. Correlations were detected between image‐extracted traits and ground truth phenotypes. Biological relevant GWAS signals were repeatedly detected under different nitrogen conditions.