Drone Phenotyping Reveals Dynamic Nitrogen Responses and Genetic Loci in Wheat

A multiseasonal field study shows that low-cost drone phenotyping combined with dynamic curve analysis can quantify how winter wheat responds to nitrogen and help identify varieties with better nitrogen responsiveness. The work tracked 54 wheat varieties under three nitrogen treatments, measured six canopy traits, modelled their seasonal changes, classified varieties into four nitrogen-responsiveness groups, and used genome-wide association to link static and dynamic phenotypes with genetic loci. The results point to a practical path for improving nitrogen sustainability in wheat breeding and production.

Inefficient nitrogen use in agriculture drives excessive fertilizer application, redundant crop growth, greenhouse gas emissions, ecosystem toxicity and risks to human health. Wheat production depends heavily on synthetic nitrogen, yet nitrogen-use efficiency (NUE) and nitrogen responsiveness are controlled by many genes and shaped by environment, soil and management. Inconsistent field phenotyping across seasons and sites also makes it hard to compare traits such as nitrogen-uptake efficiency (NupE) and nitrogen harvest index (NHI). Against these challenges, deeper research is needed into reproducible dynamic phenotyping that can capture nitrogen-response changes and connect them with yield, NUE and underlying genetics.

Researchers at Nanjing Agricultural University, the National Institute of Agricultural Botany (NIAB) and collaborators published (DOI: 10.34133/plantphenomics.0128) the study in Plant Phenomics on 22 December 2023. The team used low-cost drones and the AirMeasurer platform to phenotype 54 winter wheat varieties under 0, 180 and 270 kg N ha⁻¹. They quantified plant height, canopy coverage, normalized difference yellowness index (NDYI), visible atmospherically resistant index (VARI), 3D canopy index (3DCI) and angular second moment (ASM). They then built dynamic profiles, trained a random forest-nitrogen response (RF-NRES) model and performed genome-wide association study (GWAS) with the Wheat55K single-nucleotide polymorphism (SNP) array.

The study combined static phenotypes from 10 drone flights with dynamic phenotypes derived from curve fitting. Gaussian, Fourier and Weibull functions produced profile curves for six traits across the season, and compound growth rates (CGRs) were calculated in five-day windows from 1 to 25 days after fertilization (DAF). Using 12 yield-production and N-utilization indices, principal component analysis (PCA) generated nitrogen efficiency comprehensive scores (NECS) and divided varieties into four groups: high-yield-high-N-utilization (HYHN), low-yield-high-N-utilization (LYHN), low-yield-low-N-utilization (LYLN) and high-yield-low-N-utilization (HYLN). A machine-learning model, RF-NRES, classified N-responsiveness groups from N-response phenotypes with 91.6% accuracy; cross-validation reached 93.0% for HYHN and 90.5% for LYLN varieties. GWAS using 44,215 SNPs showed that dynamic phenotypes associated with more significant SNPs than static traits. For example, under N0, 714 SNPs were linked to dynamic phenotypes versus 94 for static phenotypes. Candidate loci included SVP, SAR and AP2L5 under N0; NRT1.1A under N180/N270; and CAO and CA1Pase for height and VARI changes. These findings highlight dynamic phenotyping as a powerful bridge between field performance and genetic discovery.