ArticleBMC plant biology2025
UAV-based multispectral image analysis revealed stay-green haplotypes in wheat specific for different soil nitrogen levels.
Article in BMC plant biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
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4 citing papers in PubMed.
- Pre-Visual Detection of Pine Wilt Disease Using an Optimized PSRI Derived from Hyperspectral Drone Imagery.Plants (Basel, Switzerland) · 2026Article
- Deciphering the genetic basis of yield components in wheat by integrating hyperspectral-based phenomes.Plant phenomics (Washington, D.C.) · 2026Article
- Integrating UAV, environmental, and management data to improve rice nitrogen nutrition index prediction using an ensemble learning algorithm.BMC plant biology · 2026Article
- Correction: UAV-based multispectral image analysis revealed stay-green haplotypes in wheat specific for different soil nitrogen levels.BMC plant biology · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe so-called stay-green trait, a delay in onset and progression of leaf senescence, is associated with slower chlorophyll degradation and higher photosynthesis rates during maturation resulting in higher crop yields. Understanding the genetic and physiological basis of the stay-green trait and breeding cultivars with stable stay-green behaviour across a range of different nitrogen (N) conditions and specifically under low N availability can contribute to ensuring wheat yields and reducing N fertilizer application. The goal of this study was therefore to identify haplotypes associated with high stay-green capacity under different N availability conditions in wheat. A diverse set of 221 wheat cultivars was grown under three different N levels and phenotyped by uncrewed aerial vehicle (UAV)-based multispectral imaging to characterise genetic and environmental variation in stay-green. Haplotypes associated with stay-green were identified across N levels and specifically under low N availability.
resultsThe plant senescence reflectance index (PSRI) calculated from multispectral images was identified as the most specific stay-green indicator allowing for differentiation of genotypic effects due to its greater sensitivity to senescence-related changes in pigment composition and its higher reliability. We found genetic variance for stay-green and a consistent genetic correlation between stay-green and grain yield at all imaging dates and N levels within the utilised diversity panel confirming its potential as a future breeding target. Haplotype analyses revealed two favourable major allele haplotypes present in 95% of the stay-green cultivars, i.e. the top 25% of the diversity set based on PSRI values, which significantly enhance stay-green performance and grain yield. In addition, we identified a favourable minor allele haplotype specifically associated with stay-green under low N availability and capable of further increasing stay-green and grain yield when stacked onto the two favourable major allele haplotypes.
conclusionsThe newly identified stay-green haplotypes can be further used for fine-mapping and identifying the underlying genes as well as for selecting for higher stay-green and grain yield. Thereby our results can contribute to improving our understanding of the complex genetic regulation underlying stay-green in different environments and to breeding new cultivars with stable performance across N levels or specifically under low N availability.
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