Drones and 3D models unlock new genetic insights into wheat plant height

By capturing multiple height quantiles rather than a single average, the approach reveals subtle variations within crop plots, enabling more accurate genetic mapping. The technique identified 11 stable height-related genetic loci, including two potential novel ones, and yielded validated molecular markers to accelerate marker-assisted selection in wheat breeding programs.

Wheat (Triticum aestivum L.) provides about one-fifth of global caloric intake. Plant height (PH) is a key agronomic trait, influencing both yield potential and lodging resistance. Excessive height can cause plants to topple, while overly short plants may suffer reduced biomass and photosynthetic efficiency. During the “Green Revolution,” dwarfing genes boosted yields worldwide, but modern breeding still seeks optimal PH to balance productivity and resilience. Traditional field measurements—manually gauging a few representative plants—are labor-intensive, prone to human error, and fail to capture within-plot variation. Advances in high-throughput phenotyping, especially 3D canopy modeling, offer a path to more precise and unbiased PH assessments.

A study (DOI: 10.1016/j.plaphe.2025.100017) published in Plant Phenomics on 27 February 2025 by Yuntao Ma’s & Yonggui Xiao’s team, China Agricultural University & Chinese Academy of Agricultural Sciences, demonstrates that low-cost UAV cross-circling oblique imaging enables highly accurate, multi-level 3D measurement of wheat plant height, uncovering novel genetic loci and providing validated molecular markers to accelerate precision breeding.

This study employed low-altitude UAV cross-circling oblique (CCO) imaging to assess wheat plant height (PH) across multiple environments, comparing its performance with conventional nadir imaging. Both methods were conducted at the same flight altitude and overlap settings in one environment, with 24 additional plots included to increase PH variability. CCO imaging captured more complete canopy details, particularly at plot fronts, and produced denser, more accurate point clouds. PH values were extracted from 11 height quantiles, revealing consistently higher correlation coefficients and lower RMSEs for CCO imaging than nadir imaging. The 90% quantile most closely matched field-measured PH (FM-PH), while lower quantiles risked measuring stem rather than canopy height. Detailed CCO point clouds reconstructed canopy structures at the organ scale, clearly depicting spikes, though side view capture was limited when plots were closely spaced. Analysis of RIL populations showed both FM-PH and multi-level 3D-PH followed normal distributions, with strong correlations across quantiles and high broad-sense heritability (0.775–0.959 within environments; 0.975–0.982 across environments). The 90% and 92% quantiles yielded RMSEs below 2 cm in most cases, with a maximum correlation of 0.99 between FM-PH and 3D-PH. QTL mapping across seven environments identified 106 loci for FM-PH and 3D-PH, with 40 shared and 11 stable loci unique to multi-level 3D-PH. Two potential novel loci—QPhzj.caas-3A.2 and QPhzj.caas-7A.1—were converted into KASP markers, validated in natural populations, and linked to significant PH differences under varied irrigation. Candidate gene analysis pinpointed Rht5, a gibberellin-sensitive dwarfing gene on chromosome 3B, and TaGL3-5A, associated with grain length and weight on chromosome 5A, both supported by KASP validation. These results demonstrate that CCO imaging provides a precise, scalable tool for phenotyping PH, enabling more comprehensive genetic analysis than traditional methods.

The integration of UAV CCO imaging and multi-level 3D-PH analysis provides breeders with a low-cost, scalable, and precise tool for phenotyping plant height. By uncovering genetic loci that conventional measurements might miss, the method enhances the efficiency of marker-assisted selection, speeding the development of high-yield, lodging-resistant wheat varieties. Beyond wheat, this workflow could be adapted for other crops where canopy architecture and height are important, offering new opportunities in precision agriculture and crop improvement.

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References

DOI

10.1016/j.plaphe.2025.100017

Original Source URL

https://doi.org/10.1016/j.plaphe.2025.100017

Funding information

This work was funded by National Key R&D Program of China (2022ZD0115703), the National Natural Science Foundation of China (32372196, 42271319) and Pinduoduo-China Agricultural University Research Fund (PC2023A02002).

About Plant Phenomics

Science Partner Journal Plant Phenomics is an online-only Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.