Plant height (PH) and aboveground biomass (AGB) are critical agronomic traits that determine the yield potential of maize ( Zea mays L . ). However, the application of genomic selection (GS) and genome-wide association studies (GWAS) in maize breeding is often hindered by the limitations of phenotypic data collection, which is typically characterized by low throughput and inadequate accuracy. To address this challenge, we employed an unmanned aerial vehicle (UAV) equipped with LiDAR and RGB cameras for high-throughput assessment of pH and AGB in a panel of 817 maize hybrids derived from 364 inbred lines over two growing seasons. Our results demonstrated that the integration of UAV-derived LiDAR point clouds with crop surface models (CSMs) enabled robust estimation of pH across multiple years ( R 2 > 0.90). Furthermore, a three-dimensional AGB estimation model was developed using UAV-derived PH and canopy coverage (CC), achieving high estimation accuracy ( R 2 > 0.83). Subsequently, the UAV-derived PH and AGB were utilized for GS and GWAS analyses. Replicated 10-fold cross-validation showed that the mean predictability was 0.504 for PH and 0.402 for AGB across eight commonly used GS models. Moreover, of the 66,066 potential crosses derived from the 364 inbred lines, the top 200 crosses selected for AGB showed up to twice the AGB of the bottom 200 crosses. Field validation demonstrated that the mean ear weight (EW) in the AGB top group was 39.0% higher than that in the bottom group. A total of 16 and 11 significant SNPs were identified by at least two GWAS methods for PH and AGB, respectively. Based on these SNPs, 81 candidate genes were functionally annotated, six of which were simultaneously associated with both traits. The candidate gene association analysis suggested that variations in the promoter region of ZmFLA9 may affect both traits. Overall, our study highlights the potential of UAV-based high-throughput phenotyping to accelerate maize genomic breeding by enabling rapid, precise, and large-scale trait assessment.
Zhou et al. (Sun,) studied this question.