Pre-harvest sprouting (PHS) or vivipary is a major problem affecting cereal quality and grain quantity and is primarily linked to the dysregulation of abscisic acid (ABA) biosynthesis in plants. Therefore, elucidating the molecular mechanisms governing seed dormancy is crucial for developing strategies to improve crop productivity. In this study, we identified a novel viviparous maize mutant, viviparous-like 5 (vp-like5), which exhibits precocious germination in developing seeds. Through map-based cloning, we discovered that ZmCNX6, which encodes a small subunit of molybdopterin synthase essential for molybdenum cofactor (MoCo) biosynthesis, is the causal gene responsible for the vp-like5 phenotype. Biochemical assays have demonstrated significantly reduced activities of MoCo-dependent enzymes, including aldehyde oxidase (AO), xanthine dehydrogenase (XDH), and nitrate reductase (NR), in vp-like5. AO is essential for the ABA biosynthesis, and the observed ABA deficiency in vp-like5 likely drives the viviparous phenotype. Expression analysis showed that ZmCNX6 was stably expressed during seed development, indicating its significant role in seed development. Furthermore, overexpression of ZmCNX6 not only enhanced the activities of MoCo-dependent enzymes but also improved drought tolerance in maize. Collectively, our study revealed ZmCNX6 as a multifunctional hub coordinating MoCo metabolism, ABA-dependent dormancy regulation, and abiotic stress responses, offering a potential target for simultaneously mitigating vivipary and improving drought resistance in maize.

Maize (Zea mays L.) is a globally significant crop that plays a crucial role in feeding the world’s growing population. Among its various traits, plant height is particularly important as it affects yield, lodging resistance, ecological adaptability, and other important factors. Traditional methods for measuring plant height often lack cost-efficiency and accuracy. In this study, a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV) was employed to collect point cloud data from 270 doubled haploid (DH) lines. This innovative application of UAV-based LiDAR technology was explored for high-throughput phenotyping in maize breeding trials. High-density genetic maps were constructed, and plant height was assessed at both single-plant and row scales across multiple developmental stages and genetic backgrounds. The findings revealed that for many varieties and small areas, single-plant-scale estimation accuracy was superior to row-scale estimation, with R² values of 0.67 vs. 0.56 and RMSE values of 0.12 m vs. 0.17 m, respectively. Two high-density genetic maps were constructed based on SNP markers. In Sanya and Xinxiang, the F₁DH and F₂DH populations identified 12 and 20 QTLs (quantitative trait loci) for plant height, respectively. This study successfully identified and validated QTLs associated with plant height, thereby revealing novel genetic loci and candidate genes. This research highlights the potential of UAV-based remote sensing to advance precision agriculture by enabling efficient, large-scale phenotyping and gene discovery in maize breeding programs.