A UAV-based high-throughput phenotyping approach to assess time-series nitrogen responses and identify traits associated genetic components in maize

Advancements in the use of genome-wide markers have provided new 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 diversity panel were grown in a replicated incomplete block under both nitrogen-limited conditions and following conventional agronomic practices. UAV images were collected during different plant developmental stages throughout the growing season. A pipeline for extracting plot-level images, filtering images to remove non-foliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed. After applying the pipeline, about half a million 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 collected from the same plots, i.e., Vegetative Index (VEG) vs. leaf nitrogen levels (Pearson correlation coefficient, R = 0.73), Woebbecke index vs. leaf area (R = -0.52), and Visible Atmospherically Resistant Index (VARI) vs. 20 kernel weight --- a yield component trait (R = 0.40). The genome-wide association study was performed using canopy coverage and each of the VIs at each date, resulting in N = 29 unique genomic regions associated with image extracted traits from three 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 SNPs of the canopy coverage associated signals that 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.